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


Yanping Shen, Kangfeng Zheng, Chunhua Wu, Journal of Information Processing Systems Vol. 18, No. 1, pp. 146-158, Feb. 2022  

https://doi.org/10.3745/JIPS.03.0174
Keywords: Feature selection, Intrusion Detection, Kernel extreme learning machine, Parameter optimization, Particle Swarm Optimization
Fulltext:

Abstract

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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Cite this article
[APA Style]
Shen, Y., Zheng, K., & Wu, C. (2022). A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection. Journal of Information Processing Systems, 18(1), 146-158. DOI: 10.3745/JIPS.03.0174.

[IEEE Style]
Y. Shen, K. Zheng, C. Wu, "A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection," Journal of Information Processing Systems, vol. 18, no. 1, pp. 146-158, 2022. DOI: 10.3745/JIPS.03.0174.

[ACM Style]
Yanping Shen, Kangfeng Zheng, and Chunhua Wu. 2022. A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection. Journal of Information Processing Systems, 18, 1, (2022), 146-158. DOI: 10.3745/JIPS.03.0174.