Last few weeks, I was preparing some lectures on AI & Machine Learning Methods for Cybersecurity. While I have been working in the area, I haven’t taught such content in the past and this was a whole new experience for me as well. Surprisingly, I did not find much resources that provide a structured view on the subject. Below is a set of resources I found highly useful whilst preparing my lectures.
1) CS 259D Data Mining for Cyber Security, Stanford University – Autumn 2014
(course web page)
This probably is the only university course I found which is doing exactly I was preparing for. It covers a range of topics such as behavioural biometrics, deep packet inspection, and phishing detection. While it appears that the course is not offered after 2014, the topics it has covered in the last offering is comprehensive and still highly relevant. One topic I might add to this already comprehensive list of topics is probably adversarial machine learning (which was probably has not become mainstream by then).
2) Machine Learning and Security: Protecting Systems with Data and Algorithms By Clarence Chio & David Freeman
(link to the page of the book)
I enjoyed reading this book as well as trying out various example codes released with the book as Jupyter notebooks that can be found here. Major part of the book follows the format in which different machine learning concepts that are related to security are introduced with worked examples. Some interesting chapters include network traffic analytics, adversarial machine learning, and malware analysis.
3) Research paper compilations
There were several repositories that are collections of papers related to the use of machine learning in security applications.
- The Definitive Security Data Science and Machine Learning Guide: In addition to collection of papers, this website provides a compilation of blogs, datasets, books, and presentations on the subject.
- Machine-Learning-for-Cyber-Security
- Collection of Deep Learning Cyber Security Research Papers.