An intrusion detection method for internet of things based on suppressed fuzzy clustering

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...

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Vydáno v:EURASIP journal on wireless communications and networking Ročník 2018; číslo 1; s. 1 - 7
Hlavní autoři: Liu, Liqun, Xu, Bing, Zhang, Xiaoping, Wu, Xianjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 09.05.2018
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Clustering
Communications Engineering
Computer simulation
Cybersecurity
Engineering
Information Systems Applications (incl.Internet)
Internet of Things
Intrusion detection
Intrusion detection systems
Networks
Principal component analysis algorithm
Principal components analysis
Research and Challenges of Wireless Networks in Internet of Things
Signal,Image and Speech Processing
Suppressed fuzzy clustering algorithm
Internet of things
Suppressed fuzzy clustering algorithm
Principal component analysis algorithm
Intrusion detection
ISSN:1687-1499, 1687-1472, 1687-1499
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-018-1128-z