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

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)