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Securing the Edge: CatBoost Classifier Optimized by the Lyrebird Algorithm to Detect Denial of Service Attacks in Internet of Things-Based Wireless Sensor Networks

Author

Listed:
  • Sennanur Srinivasan Abinayaa

    (Department of Electronics and Communication Engineering, Dr. NGP Institute of Technology, Coimbatore 641048, India)

  • Prakash Arumugam

    (Karnavati School of Research, Karnavati University, Gujarat 382422, India)

  • Divya Bhavani Mohan

    (United World School of Computational Intelligence, Karnavati University, Gujarat 382422, India)

  • Anand Rajendran

    (Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India)

  • Abderezak Lashab

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Baoze Wei

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Josep M. Guerrero

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

Abstract

The security of Wireless Sensor Networks (WSNs) is of the utmost importance because of their widespread use in various applications. Protecting WSNs from harmful activity is a vital function of intrusion detection systems (IDSs). An innovative approach to WSN intrusion detection (ID) utilizing the CatBoost classifier (Cb-C) and the Lyrebird Optimization Algorithm is presented in this work (LOA). As is typical in ID settings, Cb-C excels at handling datasets that are imbalanced. The lyrebird’s remarkable capacity to imitate the sounds of its surroundings served as inspiration for the LOA, a metaheuristic optimization algorithm. The WSN-DS dataset, acquired from Prince Sultan University in Saudi Arabia, is used to assess the suggested method. Among the models presented, LOA-Cb-C produces the highest accuracy of 99.66%; nevertheless, when compared with the other methods discussed in this article, its error value of 0.34% is the lowest. Experimental results reveal that the suggested strategy improves WSN-IoT security over the existing methods in terms of detection accuracy and the false alarm rate.

Suggested Citation

  • Sennanur Srinivasan Abinayaa & Prakash Arumugam & Divya Bhavani Mohan & Anand Rajendran & Abderezak Lashab & Baoze Wei & Josep M. Guerrero, 2024. "Securing the Edge: CatBoost Classifier Optimized by the Lyrebird Algorithm to Detect Denial of Service Attacks in Internet of Things-Based Wireless Sensor Networks," Future Internet, MDPI, vol. 16(10), pages 1-20, October.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:10:p:381-:d:1502397
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