Probabilistic profiling of stateful data planes for adversarial testing

Abstract

Recently, there is a flurry of projects that develop data plane systems in programmable switches, and these systems perform far more sophisticated processing than simply deciding a packet's next hop (i.e., traditional forwarding). This presents challenges to existing network program profilers, which are developed primarily to handle stateless forwarding programs. We develop P4wn, a program profiler that can analyze program behaviors of stateful data plane systems; it captures the fact that these systems process packets differently based on program state, which in turn depends on the underlying stochastic traffic pattern. Whereas existing profilers can only analyze stateless network processing, P4wn can analyze stateful processing behaviors and their respective probabilities. Although program profilers have general applications, we showcase a concrete use case in detail: Adversarial testing. Unlike regular program testing, adversarial testing distinguishes and specifically stresses low-probability edge cases in a program. Our evaluation shows that P4wn can analyze complex programs that existing tools cannot handle, and that it can effectively identify edge-case traces.