On Tuning the Knobs of Distribution-Based Methods for Detecting VoIP Covert Channels

Abstract

We study the parameters (knobs) of distribution-based anomaly detection methods, and how their tuning affects the quality of detection. Specifically, we analyze the popular entropy-based anomaly detection in detecting covert channels in Voice over IP (VoIP) traffic. There has been little effort in prior research to rigorously analyze how the knobs of anomaly detection methodology should be tuned. Such analysis is, however, critical before such methods can be deployed by a practitioner. We develop a probabilistic model to explain the effects of the tuning of the knobs on the rate of false positives and false negatives. We then study the observations produced by our model analytically as well as empirically. We examine the knobs of window length and detection threshold. Our results show how the knobs should be set for achieving high rate of detection, while maintaining a low rate of false positives. We also show how the throughput of the covert channel (the magnitude of the anomaly) affects the rate of detection, thereby allowing a practitioner to be aware of the capabilities of the methodology.