Poison message failure is a mechanism that has been responsible for large-scale failures in both telecommunications and IP networks. The poison message failure can propagate in the network and cause unstable network. In this paper, we apply machine learning, data mining technique in network fault management area. We use k-nearest neighbor method to identify the poison message failure. Also we integrate the k-nearest neighbor method with message filtering approach. We also propose a “probabilistic” k-nearest neighbor method that outputs a probability distribution (rather than the identity) of the poison message. Through extensive simulations, we show that k-nearest neighbor method is very effective in identifying the responsible message type.
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Xiaojiang (James) Du is an assistant professor in Department of Computer Science, North Dakota State University. Dr. Du received his B.E. degree from Tsinghua University, Beijing, China in 1996, and his M.S. and Ph.D. degrees from University of Maryland, College Park in 2002 and 2003, respectively, all in Electrical Engineering. His research interests are wireless sensor networks, mobile ad hoc networks, network security and network management. Dr. Du is an associated editor of Wiley Journal of Wireless Communication and Mobile Computing. He is the program chair of Computer and Network Security Symposium of IEEE International Wireless Communication and Mobile Computing Conference (IWCMC) 2006. He is (was) a TPC member for many major IEEE conferences such as INFOCOM, ICC, GLOBECOM, IM, and NOMS.
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Du, X. Identifying Control and Management Plane Poison Message Failure by K-Nearest Neighbor Method. J Netw Syst Manage 14, 243–259 (2006). https://doi.org/10.1007/s10922-006-9027-8
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DOI: https://doi.org/10.1007/s10922-006-9027-8