DoH Deception: Evading ML-Based Tunnel Detection with Black-Box Attack Techniques

Ground Truth, Wed, Aug 7, 16:00 - Wed, Aug 7, 16:45 CDT This presentation is part of a graduate research project that delves into the vulnerabilities of Machine Learning (ML) models specifically designed to detect DNS Over HTTPS (DoH) tunnels. Previ...

Ground Truth, Wed, Aug 7, 16:00 - Wed, Aug 7, 16:45 CDT

This presentation is part of a graduate research project that delves into the vulnerabilities of Machine Learning (ML) models specifically designed to detect DNS Over HTTPS (DoH) tunnels. Previous research has primarily focused on developing models that prioritize accuracy and explainability. However, these studies have often overlooked the potential of adversarial attacks, leaving the models vulnerable to common adversarial attacks like black-box attacks. This presentation will demonstrate that all cutting-edge DoH tunnel detection models are vulnerable to black-box attacks. Our approach leverages real-world input data generated by DoH tunnel tools, which are constrained in the attack algorithm.

Moreover, we will show specific vulnerable features that model developers should avoid. When this feature type is considered, we successfully evaded all DoH tunnel detection models without using advanced techniques.

Notably, the audience can use the same methods to evade most Machine Learning-Based Network Intrusion Detection Systems, underlining our findings' immediate and practical implications.

People
Emanuel Valente