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Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT

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Abstract

Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, St. Martin’s Engineering College, Hyderabad, Telangana, 500100, India

    Satheesh Narayanasami

  2. Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627152, India

    Sudhakar Sengan

  3. Biology Teacher, Teaching Human Social Biology and Applied Sciences, Roots IVY International Schools, Faisalabad, 38000, Punjab, Pakistan

    Saira Khurram

  4. University of Engineering and Technology, Lahore, 39161, Punjab, Pakistan

    Farrukh Arslan

  5. Department of Information Technology, Sri Sai Ram Engineering College, Chennai, Tamil Nadu, 600044, India

    Suresh Kumar Murugaiyan

  6. Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, Tamil Nadu, 635109, India

    Regin Rajan

  7. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600048, India

    Vijayakumar Peroumal

  8. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, 201009, India

    Anil Kumar Dubey

  9. Department of Electronics and Communications Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, Karnataka, 560029, India

    Sujatha Srinivasan

  10. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, 473226, India

    Dilip Kumar Sharma

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  1. Satheesh Narayanasami
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  2. Sudhakar Sengan
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  3. Saira Khurram
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  4. Farrukh Arslan
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  5. Suresh Kumar Murugaiyan
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  6. Regin Rajan
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  7. Vijayakumar Peroumal
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  8. Anil Kumar Dubey
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  9. Sujatha Srinivasan
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  10. Dilip Kumar Sharma
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Correspondence to Sudhakar Sengan.

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Narayanasami, S., Sengan, S., Khurram, S. et al. Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT. Wireless Pers Commun 127, 1763–1785 (2022). https://doi.org/10.1007/s11277-021-08721-8

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  • DOI: https://doi.org/10.1007/s11277-021-08721-8

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