Optimized Cross-Path Attacks via Adversarial Reconnaissance

While softwarization and virtualization technologies make modern communication networks appear easier to manage, they also introduce highly complex interactions within the networks that can cause unexpected security threats. In this work, we study a particular security threat due to the sharing of links between high-security paths and low-security paths, which enables a new type of DoS attacks, called cross-path attacks, that indirectly attack a set of targeted high-security paths (target paths) by congesting the shared links through a set of attacker-controlled low-security paths (attack paths). While the feasibility of such attacks has been recently demonstrated in the context of SDN, their potential performance impact has not been characterized. To this end, we develop an approach for designing an optimized cross-path attack under a constrained total attack rate, consisting of (i) novel reconnaissance algorithms that can provide consistent estimates of the locations and parameters of the shared links via network tomography, and (ii) efficient optimization methods to design the optimal allocation of attack rate over the attack paths to maximally degrade the performance of the target paths. The proposed attack has achieved a significantly larger performance impact than its non-optimized counterparts in extensive evaluations based on multiple network settings, signaling the importance of addressing such intelligent attacks in network design. For more detail, see the full paper [4].