Analysis of traffic correlation attacks on router queues

In this paper, we develop an analytical, fluid-based framework that models how the correlation attack disrupts router queues and how it can be mitigated. Using Poisson Counter Stochastic Differential Equations (PCSDEs), our framework captures the dynamics of a router queue for special cases and gives the closed-form average router queue length as a function of the inter-flow correlation. To mitigate the correlation attack, we apply our analytical framework to model different pacing schemes including Markov ON-OFF pacing and rate limiting, which are respectively designed to break down the inter-flow correlation and suppress the peak rates of bursts. We verify that our fluid models conform to packet-level ns2 simulation results.