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

Shen, Yanping; Zheng, Kangfeng; Wu, Chunhua

doi:10.3745/jips.03.0174

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

Journal of information processing systems, v.18 no.1, 2022년, pp.146 - 158

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)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (12)

그림 Fig. 1. The mechanism of the hybrid PSO-KELM.
표 Table 1. Particle structure
그림 Fig. 2. Adjusting the values of parameters ω₁ and ω₂ on KDD99: (a) accuracy with ω₁ increased and (b) number of selected features with ω₂ increased.
그림 Fig. 3. Adjusting the values of parameters ω₁ and ω₂ on NSL: (a) accuracy with ω₁ increased and (b) number of selected features with ω₂ increased.
표 Table 2. Features used and their representations in the Kyoto dataset
그림 Fig. 4. Adjusting the values of parameters ω₁ and ω₂ on Kyoto: (a) accuracy with ω₁ increased and (b) number of selected features with ω₂ increased.
표 Table 3. Experimental results of the Grid-KELM, continue PSO-KELM, hybrid PSO-KELM and GA-KELM methods on KDD99
표 Table 4. Experimental results of the Grid-KELM, continue PSO-KELM, hybrid PSO-KELM and GA-KELM methods on NSL
표 Table 5. Experimental results of the Grid-KELM, continue PSO-KELM, hybrid PSO-KELM and GA-KELM methods on Kyoto
표 Table 6. Frequency of the selected features on KDD99 and NSL
표 Table 7. Frequency of the selected features on the Kyoto
표 Table 8. Comparison of accuracy of different models (%)

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표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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