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EFFECTIVE INTRUSION DETECTION SYSTEM USING SUPPORT VECTOR MACHINE LEARNING

Abstract

Mobile Ad hocnetworks (MANET)are self- configuring, infrastructureless, dynamic wireless networks in which the nodes are resource constrained. Intrusion Detection Systems (IDS) are used in manets to monitor activities so as to detect any intrusion in the otherwise vulnerable network. In this paper, we present efficient schemes for analyzing and optimizing the time duration for which the intrusion detection systems need to remain active in a mobile ad hoc network. A probabilistic model is proposed that makes use of cooperation between idss among neighborhood nodes to reduce their individual active time. Usually, an IDS has to run all the time on every node to oversee the network behavior. This can turn out to be a costly overhead for a battery-powered mobile device in terms of power and computational resources. Hence, in this work our aim is to reduce the duration of active time of the idss without compromising on their effectiveness. To validate our proposed approach, we model the interactions between idss as a multi-player cooperative game in which the players have partially cooperative and partially conflicting goals. We theoretically analyze this game and support it with simulation results.

Author

P.Amala,G.Gayathri,S.Dhinesh,Mr.S.Prabagar
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