Abstract
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.
Highlights
Owing to the rapid development and wide applications of the Internet of Things (IoT) techniques, security of IoT has attracted increasing attentions
The huge network data traffic is a huge challenge to intrusion detection systems as it induces high requirements on the detection efficiency so that attacks can be detected in real time
Aimed at low effectiveness of intrusion detections for IoT, an intrusion detection method for IoT based on suppressed fuzzy clustering (SFC) algorithm and principal component analysis (PCA) algorithm is proposed
Summary
Owing to the rapid development and wide applications of the Internet of Things (IoT) techniques, security of IoT has attracted increasing attentions. Based on classification methods in data mining, optimized solutions were identified by direction calculations of relevant matrices and multi-category network attacks are analyzed by multi-objective mathematical programming model [11]. An improved multi-objective genetic algorithm-based intrusion detection integrated method has been proposed [16].
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