DoH Deception: Testing the Robustness of ML-Based Tunnel Detection
High accuracy is not the same thing as robustness.[1] That was the question behind my master’s research on DNS over HTTPS (DoH) tunnel detection. Several machine-learning classifiers reported excellent performance when evaluated on conventional train/test splits. But an attacker does not need to draw another sample from the same dataset distribution. They can change how a tunnel behaves, observe the detector’s output, and search for a traffic pattern that still carries their data while the classifier labels the flow as benign.[1] ...