A Comprehensive Survey on Intrusion Detection Systems in IoT-Enabled Wireless Sensor Networks: Techniques, Challenges and future directions

The growth of Internet of Things (IoT) devices have greatly improved the capabilities and applications of wireless sensor networks (WSNs). Due to their open and modular design, wireless sensor networks supporting IoT are vulnerable to various security issues, including but not limited to unauthorized access, data mining, and competitive cyberattacks. In order to protect these networks from malicious attacks, intrusion detection system (IDS) has become an important first line of defence. This survey provides a comprehensive review of state-of-the art IDS techniques designed for IoTwireless sensor networks, focusing on classical, machine learning, and advanced deep learning. In particular, we report the recent integration of two optimization methods: Generative Adversarial Networks (GAN), Gorilla Alliance Optimization (GTO), and Coati Optimization Algorithm (COA). Despite progress, challenges such as limited resources, timely completion, and capacity building remain significant. Through critical comparative analysis, we identify gaps in current research and suggest future directions, including the use of new technologies and model engines to improve the efficiency and responsiveness of IDS against security breaches. This research is designed to provide a valuable resource for researchers and practitioners who want to develop robust and adaptable IDS systems for IoT-enabled WSNs.