文献类型：期刊文献

英文题名：An intrusion detection method for internet of things based on suppressed fuzzy clustering

作者：Liu, Liqun[1];Xu, Bing[2];Zhang, Xiaoping[3];Wu, Xianjun[4]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang, Peoples R China;[2]Guangdong Univ Petrochem Technol, Sch Comp & Elect Informat, Maoming, Peoples R China;[3]Univ Sheffield, Sch Math & Stat, Sheffield S3 7RH, S Yorkshire, England;[4]Guangdong Univ Petrochem Technol, Sch Comp Ctr, Maoming, Peoples R China

年份：2018

卷号：2018

期号：1

外文期刊名：EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING

收录：SCI-EXPANDED(收录号：WOS:000432556600002)、、EI(收录号：20182005185317)、Scopus(收录号：2-s2.0-85046679577)、WOS

基金：The authors acknowledge the Funds of Science & Technology Research of Guangdong Province, China (Grants: 2015B010128015 and 2017A040403070) and the Natural Science Foundation of Guangdong Province (Grant: 2017A030307027), China.

语种：英文

外文关键词：Internet of things; Intrusion detection; Suppressed fuzzy clustering algorithm; Principal component analysis algorithm

外文摘要：In order to improve the effectiveness of intrusion detection, an intrusion detection method of the Internet of Things (IoT) is proposed by suppressed fuzzy clustering (SFC) algorithm and principal component analysis (PCA) algorithm. In this method, the data are classified into high-risk data and low-risk data at first, which are detected by high frequency and low frequency, respectively. At the same time, the self-adjustment of the detection frequency is carried out according to the suppressed fuzzy clustering algorithm and the principal component analysis algorithm. Finally, the key factors influencing the algorithm are analyzed deeply by simulation experiment. The results shows that, compared to traditional method, this method has better adaptability.