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

Author

Listed:
  • Zhang Ling

    (Zhengzhou University of Light Industry, China)

  • Zhang Jia Hao

    (Zhengzhou University of Light Industry, China)

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.

Suggested Citation

  • Zhang Ling & Zhang Jia Hao, 2022. "Intrusion Detection Using Normalized Mutual Information Feature Selection and Parallel Quantum Genetic Algorithm," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-24, January.
    • Handle: RePEc:igg:jswis0:v:18:y:2022:i:1:p:1-24
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    References listed on IDEAS

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    1. Ahmad Al-Nawasrah & Ammar Ali Almomani & Samer Atawneh & Mohammad Alauthman, 2020. "A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 10(3), pages 17-53, July.
    2. Sagnik Anupam & Arpan Kumar Kar, 2021. "Phishing website detection using support vector machines and nature-inspired optimization algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 17-32, January.
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