Journal of Information Processing Systems

  • 제18권1호
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  • Pages.146-158
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  • 2022
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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DOI QR코드

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

  • Shen, Yanping (School of Information Engineering, Institute of Disaster Prevention) ;
  • Zheng, Kangfeng (School of Cyberspace Security, Beijing University of Posts and Telecommunications) ;
  • Wu, Chunhua (School of Cyberspace Security, Beijing University of Posts and Telecommunications)
  • 투고 : 2021.02.05
  • 심사 : 2021.08.23
  • 발행 : 2022.02.28
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초록

With the success of the digital economy and the rapid development of its technology, network security has received 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 to determine the parameter combination and the feature subset. A fitness function based on the detection rate and the number of selected features is proposed. The results show that the method can simultaneously determine the parameter values and select features. Furthermore, competitive or better accuracy can be obtained using approximately one quarter of the raw input features. Experiments proved that our method is slightly better than the genetic algorithm-based KELM model.

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과제정보

This work is supported by the Fundamental Research Funds for the Central Universities (No. ZY20215151).

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