GOA-WO-ML: Enhancing Internet of Things Security with Gannet Optimization and Walrus Optimizer-Based Machine Learning

Abstract

The rapid development of the Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) has fueled security challenges, necessitating efficient intrusion detection approaches. The computationally intensive nature and the high-dimension data preclude the direct employment of machine learning-based Intrusion Detection Systems (IDSs). This study introduces GOA-WO-ML, a robust IDS system that integrates the Gannet Optimization Algorithm (GOA) and Walrus Optimizer (WO) for feature selection and parameter tuning in machine learning algorithms. The system is tested on the NSL-KDD dataset, indicating better cyberattack detection performance. The experimental findings suggest that GOA-WO-ML improves intrusion detection accuracy, decreases false positives, and has low computational overhead compared to traditional methods. By adopting bio-inspired methods, the proposed system successfully counteracts security issues in IoT-WSNs through efficient surveillance. Future research directions include considering deep learning improvements and real-time deployment methods in dynamic environments for further intrusion detection performance.