Effective Detection of Multimedia Protocol Tunneling using Machine Learning

Multimedia protocol tunneling enables the creation of covert channels by modulating data into the input of popular multimedia applications such as Skype. To be effective, protocol tunneling must be unobservable, i.e., an adversary should not be able to distinguish the streams that carry a covert channel from those that do not. However, existing multimedia protocol tunneling systems have been evaluated using ad hoc methods, which casts doubts on whether such systems are indeed secure, for instance, for censorship-resistant communication.

In this paper, we conduct an experimental study of the unobservability properties of three state of the art systems: Facet, CovertCast, and DeltaShaper. Our work unveils that previous claims regarding the unobservability of the covert channels produced by those tools were flawed and that existing machine learning techniques, namely those based on decision trees, can uncover the vast majority of those channels while incurring in comparatively lower false positive rates. We also explore the application of semi-supervised and unsupervised machine learning techniques. Our findings suggest that the existence of manually labeled samples is a requirement for the successful detection of covert channels.