A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data receiv...

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Vydáno v:Advances in Fuzzy Systems Ročník 2018; číslo 2018; s. 1 - 12
Hlavní autoři: Wang, Ping, Tang, Xiaoming, Lei, Libiao, Qu, Hongchun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Algorithms
Analysis
Artificial intelligence
Clustering
Collaboration
Computer simulation
Cybersecurity
Energy consumption
Information processing
International conferences
Intrusion detection systems
Lightweight
Methods
Monitoring
Neural networks
Radio equipment
Remote sensors
Security software
Sensors
Weight reduction
Wireless networks
Wireless sensor networks
Wireless telecommunications equipment
ISSN:1687-7101, 1687-711X
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Shrnutí:For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.
Bibliografie:ObjectType-Article-1
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ISSN:1687-7101
1687-711X
DOI:10.1155/2018/4071851