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

Sennanur Srinivasan Abinayaa, Prakash Arumugam, Divya Bhavani Mohan, Anand Rajendran, Abderezak Lashab, Baoze Wei () and Josep M. Guerrero
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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

Future Internet, 2024, vol. 16, issue 10, 1-20

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.

Keywords: wireless sensor networks (WSNs); intrusion detection (ID); CatBoost classifier (Cb-C); lyrebird optimization algorithm (LOA); WSN-DS dataset; machine learning (ML) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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