PAINT released to the public for Research Purposes Only

A practical look at machine-learning malware traffic detection and the assumptions defenders inherit when models are applied to encrypted communications.

A clear-eyed discussion of how much vulnerability analysis can be automated, how reliable those results can be, and why the question stays hard.

A reverse-engineering note on extracting useful signal from .NET malware by programmatically recovering strings and analyst context.