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Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm

Zhang Ling, Zhang Jia Hao
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 24
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.307324
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MLA

Ling, Zhang, and Zhang Jia Hao. "Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm." IJSWIS vol.18, no.1 2022: pp.1-24. http://doi.org/10.4018/IJSWIS.307324

APA

Ling, Z. & Hao, Z. J. (2022). Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24. http://doi.org/10.4018/IJSWIS.307324

Chicago

Ling, Zhang, and Zhang Jia Hao. "Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-24. http://doi.org/10.4018/IJSWIS.307324

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Abstract

This paper presents a detection algorithm using normalized mutual information feature selection and cooperative evolution of multiple operators based on adaptive parallel quantum genetic algorithm (NMIFS MOP- AQGA). The proposed algorithm is to address the problems that the intrusion detection system (IDS) has lower the detection speed, less adaptability and lower detection accuracy. In order to achieve an effective reduction for high-dimensional feature data, the NMIFS method is used to select the best feature combination. The best features are sent to the MOP- AQGA classifier for learning and training, and the intrusion detectors are obtained. The data are fed into the detection algorithm to ultimately generate accurate detection results. The experimental results on real abnormal data demonstrate that the NMIFS MOP- AQGA method has higher detection accuracy, lower false negative rate and higher adaptive performance than the existing detection methods, especially for small samples sets.