All are welcome to attend.

Abstract:

Censorship regimes are continuously adopting and deploying state-of-the-art techniques to detect and prosecute open communication on the internet. Multimedia protocol tunneling seeks to disguise covert data communication by processing it directly through a legitimate audio/video communication system. Systems like VoIP and video streaming services use variable bitrate encoding schemes, which leak characteristics of the content they carry through packet sizes and timing. In what we call a content mismatch attack, censors can distinguish between a channel carrying the legitimate media content and one carrying covert data content. We address content mismatch attacks by introducing a novel traffic shaping technique that models the normal media content and applies its properties to the covert content. We constructed a generative machine learning model to restrict covert data transmission such that its timing properties match properties learned from real two-person conversations. Our evaluation finds that modeling the timing properties in the application layer content reduces distinguishing features in the encrypted network traffic. This mitigates content mismatch attacks on coarse-grained timing properties.