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Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

Aikaterini Mitrokotsa (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Manolis Tsagkaris ; Christos Douligeris
Proceedings of the 7th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net 2008) p. 133-144. (2008)
[Konferensbidrag, refereegranskat]

In this paper we present the design and evaluation of intrusion detection models for MANETs using supervised classification algorithms. Specifically, we evaluate the performance of the MultiLayer Perceptron (MLP), the Linear classifier, the Gaussian Mixture Model (GMM), the Naïve Bayes classifier and the Support Vector Machine (SVM). The performance of the classification algorithms is evaluated under different traffic conditions and mobility patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks. The results indicate that Support Vector Machines exhibit high accuracy for almost all simulated attacks and that Packet Dropping is the hardest attack to detect.

Nyckelord: intrusion detection, mobile ad hoc networks, MANET, MLP, SVM, Gaussian Mixture model, Naïve Bayes, classification



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Denna post skapades 2014-01-07.
CPL Pubid: 191712