Explore a graduate research presentation that uncovers vulnerabilities in Machine Learning models designed for DNS Over HTTPS (DoH) tunnel detection. Delve into the susceptibility of cutting-edge DoH tunnel detection models to black-box attacks, utilizing real-world input data generated by DoH tunnel tools. Discover specific vulnerable features that model developers should avoid, and learn how these findings can be applied to evade most Machine Learning-Based Network Intrusion Detection Systems. Gain insights into the immediate and practical implications of this research for cybersecurity professionals and ML model developers.
Overview
Syllabus
Ground Truth, Wed, Aug 7, 16:00 - Wed, Aug 7, CDT
Taught by
BSidesLV