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https://github.com/wtsxdev/machine-learning-for-cyber-security

Curated list of tools and resources related to the use of machine learning for cyber security
https://github.com/wtsxdev/machine-learning-for-cyber-security

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Curated list of tools and resources related to the use of machine learning for cyber security

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# Machine Learning for Cyber Security [![Awesom](https://3.bp.blogspot.com/-ol6mgvgyN3A/WHvHkXyG6rI/AAAAAAAAB1s/OzsvrKL7glc5I7tR4GluinTXvkM2IUgSgCLcB/s1600/machine%2Blearning%2Bfor%2Bcyber%2Bsecurity.png)](http://kalitut.com)



A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security.



## Table of Contents



 - [Datasets](#-datasets)

 - [Papers](#-papers)

 - [Books](#-books)

 - [Talks](#-talks)

 - [Tutorials](#-tutorials)

 - [Courses](#-courses)

 - [Miscellaneous](#-miscellaneous)



## [↑](#table-of-contents) Datasets



* [Samples of Security Related Dats](http://www.secrepo.com/)

* [DARPA Intrusion Detection Data Sets](https://www.ll.mit.edu/ideval/data/)

* [Stratosphere IPS Data Sets](https://stratosphereips.org/category/dataset.html)

* [Open Data Sets](http://csr.lanl.gov/data/)

* [Data Capture from National Security Agency](http://www.westpoint.edu/crc/SitePages/DataSets.aspx)

* [The ADFA Intrusion Detection Data Sets](https://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-IDS-Datasets)

* [NSL-KDD Data Sets](https://github.com/defcom17/NSL_KDD)

* [Malicious URLs Data Sets](https://sysnet.ucsd.edu/projects/url)

* [Multi-Source Cyber-Security Events](http://csr.lanl.gov/data/cyber1/)

* [Malware Training Sets: A machine learning dataset for everyone](http://marcoramilli.blogspot.cz/2016/12/malware-training-sets-machine-learning.html)



## [↑](#table-of-contents) Papers



* [Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks](https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher)

* [Outside the Closed World: On Using Machine Learning for Network Intrusion Detection](http://ieeexplore.ieee.org/document/5504793/?reload=true)

* [Anomalous Payload-Based Network Intrusion Detection](https://link.springer.com/chapter/10.1007/978-3-540-30143-1_11)

* [Malicious PDF detection using metadata and structural features](http://dl.acm.org/citation.cfm?id=2420987)

* [Adversarial support vector machine learning](https://dl.acm.org/citation.cfm?id=2339697)

* [Exploiting machine learning to subvert your spam filter](https://dl.acm.org/citation.cfm?id=1387709.1387716)

* [CAMP – Content Agnostic Malware Protection](http://www.covert.io/research-papers/security/CAMP%20-%20Content%20Agnostic%20Malware%20Protection.pdf)

* [Notos – Building a Dynamic Reputation System for DNS](http://www.covert.io/research-papers/security/Notos%20-%20Building%20a%20dynamic%20reputation%20system%20for%20dns.pdf)

* [Kopis – Detecting malware domains at the upper dns hierarchy](http://www.covert.io/research-papers/security/Kopis%20-%20Detecting%20malware%20domains%20at%20the%20upper%20dns%20hierarchy.pdf)

* [Pleiades – From Throw-away Traffic To Bots – Detecting The Rise Of DGA-based Malware](http://www.covert.io/research-papers/security/From%20throw-away%20traffic%20to%20bots%20-%20detecting%20the%20rise%20of%20dga-based%20malware.pdf)

* [EXPOSURE – Finding Malicious Domains Using Passive DNS Analysis](http://www.covert.io/research-papers/security/Exposure%20-%20Finding%20malicious%20domains%20using%20passive%20dns%20analysis.pdf)

* [Polonium – Tera-Scale Graph Mining for Malware Detection](http://www.covert.io/research-papers/security/Polonium%20-%20Tera-Scale%20Graph%20Mining%20for%20Malware%20Detection.pdf)

* [Nazca – Detecting Malware Distribution in Large-Scale Networks](http://www.covert.io/research-papers/security/Nazca%20-%20%20Detecting%20Malware%20Distribution%20in%20Large-Scale%20Networks.pdf)

* [PAYL – Anomalous Payload-based Network Intrusion Detection](http://www.covert.io/research-papers/security/PAYL%20-%20Anomalous%20Payload-based%20Network%20Intrusion%20Detection.pdf)

* [Anagram – A Content Anomaly Detector Resistant to Mimicry Attacks](http://www.covert.io/research-papers/security/Anagram%20-%20A%20Content%20Anomaly%20Detector%20Resistant%20to%20Mimicry%20Attack.pdf)

* [Applications of Machine Learning in Cyber Security](https://www.researchgate.net/publication/283083699_Applications_of_Machine_Learning_in_Cyber_Security)

* [An Investigation of Byte N-Gram Features for Malware Classification](http://www.readcube.com/articles/10.1007/s11416-016-0283-1?author_access_token=Y2ftVow3BBIXRTHYIxoCG_e4RwlQNchNByi7wbcMAY4NW74db1mhZZQDQYJ1tM7Y-KZqnwIXRhZC64F6SuX0bowkkoy4Ro-NFZSGOs2sw2kG7I6cMZb9G3I0tfGpLO_rZlh-MF7KZ2i-qxjmAi-Shw%3D%3D)



## [↑](#table-of-contents) Books



* [Data Mining and Machine Learning in Cybersecurity](http://amzn.to/2iuWdYX)

* [Machine Learning and Data Mining for Computer Security](http://amzn.to/2jnCHBs)

* [Network Anomaly Detection: A Machine Learning Perspective](http://amzn.to/2jlPsgm)

* [Machine Learning for Hackers: Case Studies and Algorithms to Get You Started](http://amzn.to/2jyBZPo)



## [↑](#table-of-contents) Talks



* [Using Machine Learning to Support Information Security](https://www.youtube.com/watch?v=tukidI5vuBs)

* [Defending Networks with Incomplete Information](https://www.youtube.com/watch?v=36IT9VgGr0g)

* [Applying Machine Learning to Network Security Monitoring](https://www.youtube.com/watch?v=vy-jpFpm1AU)

* [Measuring the IQ of your Threat Intelligence Feeds](https://www.youtube.com/watch?v=yG6QlHOAWiE)

* [Data-Driven Threat Intelligence: Metrics On Indicator Dissemination And Sharing](https://www.youtube.com/watch?v=6JMEKnes-w0)

* [Applied Machine Learning for Data Exfil and Other Fun Topics](https://www.youtube.com/watch?v=dGwH7m4N8DE)

* [Secure Because Math: A Deep-Dive on ML-Based Monitoring](https://www.youtube.com/watch?v=TYVCVzEJhhQ)

* [Machine Duping 101: Pwning Deep Learning Systems](https://www.youtube.com/watch?v=JAGDpJFFM2A)

* [Delta Zero, KingPhish3r – Weaponizing Data Science for Social Engineering](https://www.youtube.com/watch?v=l7U0pDcsKLg)

* [Defeating Machine Learning What Your Security Vendor Is Not Telling You](https://www.youtube.com/watch?v=oiuS1DyFNd8)

* [CrowdSource: Crowd Trained Machine Learning Model for Malware Capability Det](https://www.youtube.com/watch?v=u6a7afsD39A)

* [Defeating Machine Learning: Systemic Deficiencies for Detecting Malware](https://www.youtube.com/watch?v=sPtbDUJjhbk)

* [Packet Capture Village – Theodora Titonis – How Machine Learning Finds Malware](https://www.youtube.com/watch?v=2cQRSPFSY-s)

* [Build an Antivirus in 5 Min – Fresh Machine Learning #7. A fun video to watch](https://www.youtube.com/watch?v=iLNHVwSu9EA&t=245s)

* [Hunting for Malware with Machine Learning](https://www.youtube.com/watch?v=zT-4zdtvR30)

* [Machine Learning for Threat Detection](https://www.youtube.com/watch?v=qVwktOa-F34)

* [Machine Learning and the Cloud: Disrupting Threat Detection and Prevention](https://www.youtube.com/watch?v=fRklX97iGIw)

* [Fraud detection using machine learning & deep learning](https://www.youtube.com/watch?v=gHtN4jU69W0)

* [The Applications Of Deep Learning On Traffic Identification](https://www.youtube.com/watch?v=B7OKgC3AJVM)

* [Defending Networks With Incomplete Information: A Machine Learning Approach](https://www.youtube.com/watch?v=_0CRSF6yPB4)

* [Machine Learning & Data Science](https://vimeo.com/112702666)



## [↑](#table-of-contents) Tutorials



* [Click Security Data Hacking Project](http://clicksecurity.github.io/data_hacking/)

* [Using Neural Networks to generate human readable passwords](http://fsecurify.com/using-neural-networks-to-generate-human-readable-passwords/)

* [Machine Learning based Password Strength Classification](http://fsecurify.com/machine-learning-based-password-strength-checking/)

* [Using Machine Learning to Detect Malicious URLs](http://fsecurify.com/using-machine-learning-detect-malicious-urls/)

* [Big Data and Data Science for Security and Fraud Detection](http://www.kdnuggets.com/2015/12/big-data-science-security-fraud-detection.html)

* [Using deep learning to break a Captcha system](https://deepmlblog.wordpress.com/2016/01/03/how-to-break-a-captcha-system/)

* [Data mining for network security and intrusion detection](https://www.r-bloggers.com/data-mining-for-network-security-and-intrusion-detection/)

* [An Introduction to Machine Learning for Cybersecurity and Threat Hunting](http://blog.sqrrl.com/an-introduction-to-machine-learning-for-cybersecurity-and-threat-hunting)



## [↑](#table-of-contents) Courses



* [Data Mining for Cyber Security by Stanford](https://web.stanford.edu/class/cs259d)



## [↑](#table-of-contents) Miscellaneous



* [System predicts 85 percent of cyber-attacks using input from human experts](https://news.mit.edu/2016/ai-system-predicts-85-percent-cyber-attacks-using-input-human-experts-0418)

* [A list of open source projects in cyber security using machine learning](http://www.mlsecproject.org/#open-source-projects)



Please have a look at

* [Best Hacking Books](http://www.kalitut.com/2016/12/best-ethical-hacking-books.html)

* [Best Reverse Engineering Books](http://www.kalitut.com/2017/01/Best-reverse-engineering-books.html)

* [Best Machine learning Books](http://www.kalitut.com/2017/01/machine-learning-book.html)

* [Best 5 books Programming Books](http://www.kalitut.com/2017/01/Top-Programming-Books.html)

* [Best Java Books](http://www.kalitut.com/2017/01/Best-Java-Programming-Books.html)