An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System

Zhang Ling, Zhang Jia Hao

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.308469

MLA

Ling, Zhang, and Zhang Jia Hao. "An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System." IJSWIS vol.18, no.1 2022: pp.1-25. http://doi.org/10.4018/IJSWIS.308469

APA

Ling, Z. & Hao, Z. J. (2022). An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-25. http://doi.org/10.4018/IJSWIS.308469

Chicago

Ling, Zhang, and Zhang Jia Hao. "An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-25. http://doi.org/10.4018/IJSWIS.308469

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Abstract

The intrusion detection system (IDS) has lower speed, less adaptability and lower detection accuracy especially for small samples sets. This paper presents a detection model based on normalized mutual antibodies information feature selection and adaptive quantum artificial immune with cooperative evolution of multiple operators (NMAIFS MOP-AQAI). First, for a high intrusion speed, the NMAIFS is used to achieve an effective reduction for high-dimensional features. Then, the best feature vectors are sent to the MOP-AQAI classifier, in which, vaccination strategy, the quantum computing, and cooperative evolution of multiple operators are adopted to generate excellent detectors. Lastly, the data is fed into NMAIFS MOP-AQAI and ultimately generates accurate detection results. The experimental results on real abnormal data demonstrate that the NMAIFS MOP-AQAI has higher detection accuracy, lower false negative rate and a higher adaptive performance than the existing anomaly detection methods, especially for small samples sets.