A reading list for the paranoid data scientist

What it does

This is a classic awesome-list — a curated index of machine-learning resources aimed at cybersecurity. It catalogs datasets (from DARPA intrusion logs to Android malware corpora), academic papers, books, talks, tutorials, and courses. Think of it as a bibliography with working hyperlinks.

The interesting bit

The list leans heavily into the data side of the problem. Roughly a third of the entries are datasets — EMBER for static PE malware, BODMAS, AWID for wireless intrusion, historical DARPA sets — which reflects how starved security ML practitioners are for labeled, high-fidelity training material. The papers span a wide arc: DNS reputation systems, PDF malware classifiers, PowerShell command detection, even adversarial attacks on your own spam filter.

Key highlights

• 20+ datasets covering network traffic, malware binaries, phishing, spam, and system logs
• Papers on both using ML for defense and attacking ML classifiers (adversarial SVMs, evading PDF malware detectors)
• Includes practical guides like SEI’s “Machine Learning in Cybersecurity: A Guide” and O’Reilly’s “Machine Learning and Security”
• Several Russian-language papers on data mining for intrusion detection, suggesting the maintainer casts a wide net
• Curated enough to have a CONTRIBUTING.md; not so curated that it feels gatekept

Caveats

• No clear quality ranking or annotation beyond the link itself — you still need to read the abstracts
• Some links are to paywalled publishers or time-sensitive conference sites; link rot is inevitable
• The “Miscellaneous” section was truncated in the source, so its depth is unclear

Verdict

Worth bookmarking if you’re a security researcher pivoting to ML, or an ML engineer suddenly asked to “do something about the logs.” Pure developers looking for drop-in libraries will leave disappointed — this is a map, not a toolbox.