The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.08 - August (2010 vol.9)
pp: 1119-1133
Iordanis Koutsopoulos , University of Thessaly, Volos, Greece
Mingyan Li , Boeing, Seattle, WA
ABSTRACT
We consider a scenario where a sophisticated jammer jams an area in which a single-channel random-access-based wireless sensor network operates. The jammer controls the probability of jamming and the transmission range in order to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by the network (namely by a monitoring node), and a notification message is transferred out of the jammed region. The jammer is detected by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network defends itself by computing the channel access probability to minimize the jamming detection plus notification time. The necessary knowledge of the jammer in order to optimize its benefit consists of knowledge about the network channel access probability and the number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of each other and the case where the jammer and the network lack this knowledge. The latter is captured by formulating and solving optimization problems where the attacker and the network respond optimally to the worst-case or the average-case strategies of the other party. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a meaningful heuristic algorithm for an efficient jamming strategy. Our results provide valuable insights about the structure of the jamming problem and associated defense mechanisms and demonstrate the impact of knowledge as well as adoption of sophisticated strategies on achieving desirable performance.
INDEX TERMS
Jamming, security, jamming detection and mitigation, optimization, wireless multiple access, wireless sensor network.
CITATION
Iordanis Koutsopoulos, Mingyan Li, "Optimal Jamming Attack Strategies and Network Defense Policies in Wireless Sensor Networks", IEEE Transactions on Mobile Computing, vol.9, no. 8, pp. 1119-1133, August 2010, doi:10.1109/TMC.2010.75
REFERENCES
[1] M. Li, I. Koutsopoulos, and R. Poovendran, "Optimal Jamming Attacks and Defense Policies in Wireless Sensor Networks," Proc. IEEE INFOCOM, 2007.
[2] M. Raya, J.-P. Hubaux, and I. Aad, "DOMINO: A System to Detect Greedy Behavior in IEEE 802.11 Hotspots," Proc. Second Int'l Conf. Mobile Systems, Applications and Services (MobiSys '04), 2004.
[3] P. Kyasanur and N. Vaidya, "Selfish MAC Layer Misbehavior in Wireless Networks," IEEE Trans. Mobile Computing, vol. 4, no. 5, pp. 502-516, Sept./Oct. 2005.
[4] S. Radosavac, I. Koutsopoulos, and J.S. Baras, "A Framework for MAC Protocol Misbehavior Detection in Wireless Networks," Proc. ACM Workshop Wireless Security (WiSe), 2005.
[5] A.D. Wood and J.A. Stankovic, "Denial of Service in Sensor Networks," Computer, vol. 35, no. 10, pp. 54-62, Oct. 2002.
[6] R. Negi and A. Perrig, "Jamming Analysis of MAC Protocols," Carnegie Mellon Technical Memo, 2003.
[7] R. Mallik, R. Scholtz, and G. Papavassilopoulos, "Analysis of an On-Off Jamming Situation as a Dynamic Game," IEEE Trans. Comm., vol. 48, no. 8, pp. 1360-1373, Aug. 2000.
[8] J. Jung, V. Paxson, A.W. Berger, and H. Balakrishnan, "Fast Portscan Detection Using Sequential Hypothesis Testing," Proc. IEEE Symp. Security and Privacy, 2004.
[9] V. Coskun, E. Cayirci, A. Levi, and S. Sancak, "Quarantine Region Scheme to Mitigate Spam Attacks in Wireless Sensor Networks," IEEE Trans. Mobile Computing, vol. 5, no. 8, pp. 1074-1086, Aug. 2006.
[10] Y.W. Law, L. van Hoesel, J. Doumen, P. Hartel, and P. Havinga, "Energy-Efficient Link-Layer Jamming Attacks Against Wireless Sensor Network MAC Protocols," ACM Trans. Sensor Networks, vol. 5, no. 1, pp. 1-38, Feb. 2009.
[11] G. Lin and G. Noubir, "On Link-Layer Denial of Service in Data Wireless LANs," Wiley J. Wireless Comm. and Mobile Computing, vol. 5, no. 3, pp. 273-284, May 2005.
[12] M. Cagalj, S. Capkun, and J.-P. Hubaux, "Wormhole-Based Anti-Jamming Techniques in Sensor Networks," IEEE Trans. Mobile Computing, vol. 6, no. 1, pp. 1-15, Jan. 2007.
[13] W. Xu, W. Trappe, Y. Zhang, and T. Wood, "The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks," Proc. ACM MobiHoc, 2005.
[14] W. Xu, T. Wood, W. Trappe, and Y. Zhang, "Channel Surfing: Defending Wireless Sensor Networks from Interference," Proc. IEEE Int'l Conf. Information Processing in Sensor Networks (IPSN), 2007.
[15] J.M. McCune, E. Shi, A. Perrig, and M.K. Reiter, "Detection of Denial-of-Message Attacks on Sensor Network Broadcasts," Proc. IEEE Symp. Security and Privacy, 2005.
[16] D.P. Bertsekas and R.G. Gallager, Data Networks, second ed. Prentice Hall, 1992.
[17] C.D.M. Cordeiro and D.P. Agrawal, Ad Hoc and Sensor Networks: Theory and Applications. World Scientific, 2006.
[18] A. Wald, Sequential Analysis. Wiley, 1947.
[19] V.P. Dragalin, A.G. Tartakovsky, and V.V. Veeravalli, "Multihypothesis Sequential Probability Ratio Tests—Part I: Asymptotic Optimality," IEEE Trans. Information Theory, vol. 45, no. 7, pp. 2448-2461, Nov. 1999.
[20] C.W. Helstrom, Elements of Signal Detection and Estimation. Prentice-Hall, 1995.
[21] A.M. Mathai, An Introduction to Geometrical Probability. Gordan and Breach Science Publishers, 1999.
33 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool