A Feature-Flux Traffic Camouflage Method based on Twin Gaussian Process

Abstract

Recent work has shown that the properties of network traffic may reveal some patterns (such as, payload size, packet interval, etc.) that can expose users' identities and their private information. The existing defense approaches, such as traffic morphing, protocol tunneling, still suffer from revealing the special traffic pattern. To address this problem, we propose a feature-flux traffic camouflage method (FFTC). FFTC forecasts the pattern of normal traffic via twin Gaussian process(TGP), and dynamically change the on-going traffic feature based on the learned traffic pattern to conceal the camouflaged traffic in the normal traffic. TGP-based traffic forecasting makes FFTC more sensitive to the feature dynamics of normal traffic. Then, the camouflaged traffic can always synchronize with the normal traffic pattern in real time. Furthermore, FFTC can learn multiple traffic patterns from different kinds of normal traffic, and dynamically change the camouflaged traffic pattern to achieve the feature-flux ability. From the experimental results, FFTC improves the indistinguishability of the camouflaged traffic and the normal traffic, and the feature-flux of the camouflaged traffic mitigates the traffic analysis attack effectively.