标题:A multivariate classification algorithm for malicious node detection in large-scale WSNs
作者:Dai, Hongjun ;Liu, Huabo ;Jia, Zhiping ;Chen, Tianzhou
通讯作者:Dai, H
作者机构:[Dai, Hongjun ;Liu, Huabo ;Jia, Zhiping ] Department of Computer Science and Technology, Shandong University, Jinan, Shandong, China;[Chen, Tianzhou ] 更多
会议名称:11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom-2012
会议日期:25 June 2012 through 27 June 2012
来源:Proc. of the 11th IEEE Int. Conference on Trust, Security and Privacy in Computing and Communications, TrustCom-2012 - 11th IEEE Int. Conference on Ubiquitous Computing and Communications, IUCC-2012
出版年:2012
页码:239-245
DOI:10.1109/TrustCom.2012.42
关键词:Malicious Node Detection; Multivariate Classification; NS2; WSN
摘要:WSN is a distributed network exposed to an open environment, which is vulnerable to malicious nodes. To find out malicious nodes among a WSN with mass sensor nodes, this paper presents a malicious detection method based on multi-variate classification. Given the types of a few sensor nodes, it extracts sensor nodes' preferences related with the known types of malicious node, establishes the sample space of all sensor nodes that participate in network activities. Then, according to the study on the type-known sensor nodes' samples based on the multivariate classification algorithm, a classifier is generated, and all of the unknown-type sensor nodes are classified. The experiment results show that as long as the value of sensor nodes preferences and the number of active sensor nodes is stable, the false detection rate is stabilized under 0.5%. © 2012 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:5
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868121729&doi=10.1109%2fTrustCom.2012.42&partnerID=40&md5=f1382da2b96890efdf7952e40147856e
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