https://github.com/13o-bbr-bbq/machine_learning_security

Source code about machine learning and security.
https://github.com/13o-bbr-bbq/machine_learning_security

Last synced: 6 days ago
JSON representation

Source code about machine learning and security.

• Host: GitHub
• URL: https://github.com/13o-bbr-bbq/machine_learning_security
• Owner: 13o-bbr-bbq
• Created: 2017-05-01T20:33:43.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-09-02T00:13:24.000Z (over 2 years ago)
• Last Synced: 2024-11-30T03:05:13.232Z (13 days ago)
• Language: Python
• Size: 58.8 MB
• Stars: 1,983
• Watchers: 154
• Forks: 657
• Open Issues: 46
• Metadata Files:
  • Readme: README.md
  • Security: Security_and_MachineLearning/Chap1_IntrusionDetection.md

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README

        
# Machine Learning and Security

Source codes about machine learning and security.

## Line up.

 * [Cyber security and Machine Learning course](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/Security_and_MachineLearning)  

 The elementary training course of Machine learning for security engineer.  

 * [Vulnerabilties of Machine Learning](https://github.com/13o-bbr-bbq/machine_learning_security/blob/master/Vulnerabilities_of_ML/)  

 Summary of Machine Learning vulnerability.  

 * [Analytics](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/Analytics)  

 Analyzing packet capture data using k-means.  

 * [CNN_test](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/CNN_test)  

 Generate adversarial example against CNN.  

 * [Deep Exploit](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/DeepExploit)  

 Fully automatic penetration test tool using Machine Learning.  

 Deep Exploit was presented at **[Black Hat USA 2018 Arsenal](https://www.blackhat.com/us-18/arsenal/schedule/index.html#deep-exploit-11908)** , **[Black Hat EURO 2018 Arsenal](https://www.blackhat.com/eu-18/arsenal/schedule/index.html#deep-exploit-fully-automatic-penetration-test-tool-using-machine-learning-13320)** and **[DEF CON 26! AI Village](https://aivillage.org/posts/accepted-talks/)**.  

* [GyoiThon](https://github.com/gyoisamurai/GyoiThon)  

 Next generation penetration test tool.  

 GyoiThon was presented at **[Black Hat ASIA 2018 Arsenal](https://www.blackhat.com/asia-18/arsenal/schedule/index.html#gyoithon-9651)** , **[Black Hat ASIA 2019 Arsenal](https://www.blackhat.com/asia-19/arsenal/schedule/index.html#gyoithon-penetration-testing-using-machine-learning-14359)** and **[DEF CON 26! Demo Labs](https://www.defcon.org/html/defcon-26/dc-26-demolabs.html)**.  

 * [DeepGenerator](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/Generator)  

 Fully automatically generate numerous injection codes for web application assessment using Genetic Algorithm and Generative Adversarial Networks.  

 * [Recommender](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/Recommender)  

 Recommend optimal injection code for detecting web app vulnerabilities.

 * [SAIVS (Spider Artificial Intelligence Vulnerability Scanner)](https://github.com/13o-bbr-bbq/machine_learning_security/tree/master/Saivs)  

 SAIVS is an artificial intelligence to find vulnerabilities in Web applications.  

 SAIVS was presented at **[Black Hat ASIA 2016 Arsenal](http://www.blackhat.com/asia-16/arsenal.html#saivs-spider-artificial-intelligence-vulnerability-scanner)**.  

## Contact us

Isao Takaesu  

[email protected]  

[https://twitter.com/bbr_bbq](https://twitter.com/bbr_bbq)