Paper Title:
A Novel Method Based on Clustering Algorithm and SVM for Anomaly Intrusion Detection of Wireless Sensor Networks
  Abstract

Based on the principle that the same class is adjacent, an anomaly intrusion detection method based on K-means and Support Vector Machine (SVM) is presented. In order to overcome the disadvantage that k-means algorithm requires initializing parameters, this paper proposes an improved K-means algorithm with a strategy of adjustable parameters. According to the location of wireless sensor networks (WSN), we can obtain clustering results by applying improved K-means algorithm to WSN, and then SVM algorithm is applied to different clusters for anomaly intrusion detection. Simulation results show that the proposed method can detect abnormal behaviors efficiently and has high detection rate and low false positive rate than the current typical intrusion detection schemes of WSN.

  Info
Periodical
Chapter
Chapter 7: Computer Application in Design and Manufacturing (1)
Edited by
Dongye Sun, Wen-Pei Sung and Ran Chen
Pages
3745-3749
DOI
10.4028/www.scientific.net/AMM.121-126.3745
Citation
Z. H. Xiao, Z. G. Chen, X. H. Deng, "A Novel Method Based on Clustering Algorithm and SVM for Anomaly Intrusion Detection of Wireless Sensor Networks", Applied Mechanics and Materials, Vols. 121-126, pp. 3745-3749, 2012
Online since
October 2011
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