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

摘要：

　　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.

关键词： wireless sensor networks anomaly detection numbers of clustering improved k-means algorithm SVM algorithm

作者: Zhenghong Xiao Zhigang Chen Xiaoheng Deng

作者单位: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665,China;School School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665,China

会议类型: 国际会议

会议名称: the Second International Conference on Frontiers of Manufacturing and Design Science(第二届制造与设计科学国际会议(ICFMD 2011))

会议地点: 台湾

会议语种:英文

页码: 3745-3749

在线出版日期: 2011-12-11（万方平台首次上网日期，不代表论文的发表时间）