Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

Details Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms

2008-07-13

arxiv.org/abs/0807.2049v1

Computer Science

Cryptography and Security

12 pages, 7 figures, presented at MedHocNet 2008

Scientific paper

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 Naive 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.

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