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A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection

Yanping Shen|Kangfeng Zheng|Chunhua Wu
한국정보처리학회|2022.02|JIPS(Journal of Information Processing Systems) (Journal of Information Processing Systems)|제18권 제1호|pp. 146-158|DOI 10.3745/JIPS.03.0174

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키워드

Feature SelectionIntrusion DetectionKernel Extreme Learning MachineParameter OptimizationParticle Swarm Optimization

초록

With the success of the digital economy and the rapid development of its technology, network security hasreceived increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine(KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together todetermine the parameter combination and the feature subset. A fitness function based on the detection rate andthe number of selected features is proposed. The results show that the method can simultaneously determinethe parameter values and select features. Furthermore, competitive or better accuracy can be obtained usingapproximately one quarter of the raw input features. Experiments proved that our method is slightly better thanthe genetic algorithm-based KELM model.

Abstract

With the success of the digital economy and the rapid development of its technology, network security hasreceived increasing attention. Intrusion detection technology has always been a focus and hotspot of research. A hybrid model that combines particle swarm optimization (PSO) and kernel extreme learning machine(KELM) is presented in this work. Continuous-valued PSO and binary PSO (BPSO) are adopted together todetermine the parameter combination and the feature subset. A fitness function based on the detection rate andthe number of selected features is proposed. The results show that the method can simultaneously determinethe parameter values and select features. Furthermore, competitive or better accuracy can be obtained usingapproximately one quarter of the raw input features. Experiments proved that our method is slightly better thanthe genetic algorithm-based KELM model.

참고문헌 (38)

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