Title: Performance analysis of machine learning algorithms for intrusion detection in MANETs

Authors: Yibo Jiang; Yu-Chen Wang; Wan-Liang Wang; Zhen Zhang; Qiong Chen

Addresses: College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China ' College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China ' College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China ' College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China ' College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

Abstract: Mobile Ad-hoc network (MANET) has become an important technology in recent years and the corresponding security problems are getting more and more attention. In this paper, we apply seven well-known machine learning algorithms to detect intrusions in MANETs. We have generated training data under various simulation parameters. We also propose a new measure method which uses five new features to represent the network traffic. The analysis results show that the multilayer perceptron, logistic regression and Support Vector Machine (SVM) have the best performance and the logistic regression and SVM also get very little time to train the classification model.

Keywords: MANETs; mobile ad-hoc networks; intrusion detection; machine learning; performance evalaution; network security; mobile networks; simulation; multilayer perceptron; logistic regression; support vector machines; SVM.

DOI: 10.1504/IJWMC.2013.057396

International Journal of Wireless and Mobile Computing, 2013 Vol.6 No.5, pp.501 - 507

Received: 17 May 2013
Accepted: 27 Jun 2013
Published online: 16 Oct 2014 *

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