TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection |
| 영문제목(English Title) |
A Nature-inspired Multiple Kernel Extreme Learning Machine Model for Intrusion Detection |
| 저자(Author) |
Yanping Shen
Kangfeng Zheng
Chunhua Wu
Yixian Yang
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| 원문수록처(Citation) |
VOL 14 NO. 02 PP. 0702 ~ 0723 (2020. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.
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| 키워드(Keyword) |
Meta-heuristics
Kernel extreme learning machine
Multiple kernel learning
Intrusion detection
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| 파일첨부 |
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