Network Interdiction Using Adversarial Traffic Flows
Name
CV_C_220.pdf
Description
Accepted version
Size
1.69 MB
Format
Adobe PDF
Checksum (MD5)
4ec93412b921e9638c68b1b52c81e1ba
Author(s)
Fu, Xinzhe.
Modiano, Eytan H
Date Issued
April 2019
Journal
IEEE INFOCOM 2019
Publisher
IEEE
Citation
Fu, Xinzhe and Eytan Modiano. “Network Interdiction Using Adversarial Traffic Flows.” IEEE INFOCOM 2019, Paris, April 29-May 2, 2019, IEEE © 2019 The Author(s)
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/INFOCOM.2019.8737475
