Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms
Paper in proceedings, 2008

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.

Naïve Bayes

SVM

Gaussian Mixture model

classification

intrusion detection

MANET

mobile ad hoc networks

MLP

Author

Aikaterini Mitrokotsa

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Manolis Tsagkaris

Christos Douligeris

Proceedings of the 7th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net 2008)

133-144

Areas of Advance

Information and Communication Technology

Subject Categories

Computer and Information Science

ISBN

978-0-387-09489-2

More information

Created

10/6/2017