JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection |
| 영문제목(English Title) |
A Hybrid PSO-BPSO Based Kernel Extreme Learning Machine Model for Intrusion Detection |
| 저자(Author) |
Yanping Shen
Kangfeng Zheng
Chunhua Wu
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| 원문수록처(Citation) |
VOL 18 NO. 01 PP. 0146 ~ 0158 (2022. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English 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. |
| 키워드(Keyword) |
Feature selection
Intrusion Detection
Kernel extreme learning machine
Parameter optimization
Particle Swarm Optimization
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| 파일첨부 |
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