Network Interdiction Using Adversarial Traffic Flows

Traditional network interdiction refers to the problem of an interdictor trying to reduce the throughput of network users by removing network edges. In this paper, we propose a new paradigm for network interdiction that models scenarios, such as stealth DoS attack, where the interdiction is performed through injecting adversarial traffic flows. Under this paradigm, we first study the deterministic flow interdiction problem, where the interdictor has perfect knowledge of the operation of network users. We show that the problem is highly inapproximable on general networks and is NP-hard even when the network is acyclic. We then propose an algorithm that achieves a logarithmic approximation ratio and quasi-polynomial time complexity for acyclic networks through harnessing the submodularity of the problem. Next, we investigate the robust flow interdiction problem, which adopts the robust optimization framework to capture the case where definitive knowledge of the operation of network users is not available. We design an approximation framework that integrates the aforementioned algorithm, yielding a quasi-polynomial time procedure with poly-logarithmic approximation ratio for the more challenging robust flow interdiction. Finally, we evaluate the performance of the proposed algorithms through simulations, showing that they can be efficiently implemented and yield near-optimal solutions

Fundamental limit of network flow attacks

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 107-110).A network flow-based attack refers to a cyber-attack where the adversary seeks to block user traffic from transmission by sending adversarial traffic that reduces the available user capacity. In this thesis, we explore the fundamental limits of network flow attacks by investigating its feasibility region defined by the minimum resource required for a successful attack and designing optimal attacking strategies that achieve the feasibility region. First, we consider the case where the target network uses fixed-path routing and the adversary injects traffic into the network, encroaching the capacity of the network links and thus reducing the capacity available to network users on the fixed paths. We propose a new network interdiction paradigm that captures this phenomenon by modeling the network as a capacitated graph with the user throughput given by the max-flow value on the fixed user paths.The adversary injects interdicting flows that reduces the capacity of the links (and hence the user throughput), and seeks to maximize the throughput reduction caused by the adversarial injection under a given flow budget. We show the NP-hardness of the problem of maximizing throughput reduction, and propose an efficient approximation algorithm that yields near optimal interdicting flows within a logarithmic factor by harnessing the submodularity of the problem. We further extend the algorithm to an approximation framework that can deal with the situation where the adversary does not have deterministic knowledge of the set of user paths but aims to maximize the worst case throughput reduction given that the set of user paths lies in certain collection of paths. Next, we turn to the scenario where the target network employs dynamic routing mechanisms such as Join-the-Shortest-Queue (JSQ) or Max-Weight.We start from single-hop server farm under JSQ routing, where the adversary attacks by injecting adversarial traffic to servers with the objective of blocking user traffic, i.e., causing user traffic to experience unbounded delay. We first characterize the feasibility region of the attack by presenting a necessary and sufficient condition on the rate of adversarial traffic rate for the attack to be successful. We then propose an adversarial injection policy that is, (i) optimal: it achieves a successful attack whenever the adversarial traffic rate is inside the feasibility region and (ii) oblivious: it does not rely on any knowledge of the network statistics. We further evaluate the performance of the injection policy. Finally, we extend our results to multi-hop.network employing Max-Weight routing.by Xinzhe Fu.S.M.S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautic