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A Hybrid Metaheuristic Algorithm for Features Dimensionality Reduction in Network Intrusion Detection System

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

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Abstract

The advent of the Internet computer, and thus the amounts of connected computers in the last few decades, has opened vast quantities of intelligence to attackers and intruders. Firewalls are designed to identify, and block potentially harmful incoming traffic based on a predefined rule set. But, as attack tactics evolve, it becomes more difficult to differentiate anomalous traffic from regular traffic. Numerous detection strategies using machine-learning approaches have been suggested. However, there are issues with the high dimensional data of network traffic, the performance accuracy, and the high rate of false-positive and false-negative. In this paper, we propose a hybrid metaheuristic features dimensionality reduction method for Intrusion Detection Systems (IDSs). We used metaheuristic Bat algorithm for feature selection. The Bat algorithm selects sixteen (16) attributes. Subsequently, RNS was used to obtain the residues of the sixteen features selected. Then, the PCA was used to get the residues by extracting it. The experimental analysis was performed on NSLKDD dataset. The propose Bat-RNS + PCA + RF achieved 98.95% accuracy, sensitivity of 99.40% and F-score of 97.70%. The findings were also benchmarked with existing studies and our results were superior.

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Authors and Affiliations

  1. Department of Computer Science, Kwara State University, Malete, Nigeria

    Bukola Fatimah Balogun & Kazeem Alagbe Gbolagade

  2. Department of Computer Science, Landmark University, Omu-Aran, Nigeria

    Micheal Olaolu Arowolo

  3. School of Information Technology and Computing, American University of Nigeria, Yola, Nigeria

    Yakub Kayode Saheed

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  1. Bukola Fatimah Balogun
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  2. Kazeem Alagbe Gbolagade
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  3. Micheal Olaolu Arowolo
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  4. Yakub Kayode Saheed
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Correspondence to Yakub Kayode Saheed .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Balogun, B.F., Gbolagade, K.A., Arowolo, M.O., Saheed, Y.K. (2021). A Hybrid Metaheuristic Algorithm for Features Dimensionality Reduction in Network Intrusion Detection System. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12957. Springer, Cham. https://doi.org/10.1007/978-3-030-87013-3_8

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