Securing Wireless Sensor Networks from Intrusions Using Machine Learning-Based Detection and Response

Wireless Sensor Networks (WSN s) are widely used in various applications, including health care, environmental monitoring, and industrial automation. However, their distributed nature and limited resources make them vulnerable to various security threats. An Intrusion Detection System (IDS) plays a critical role in identifying and mitigating unauthorized activities within WSNs. This project focuses on developing an IDS using machine learning techniques to enhance the detection accuracy and efficiency of identifying potential intrusions. By leveraging algorithms such as decision trees, support vector machines, or neural networks, the system can classify network behaviour as normal or malicious [1]. The proposed approach is evaluated on benchmark datasets to ensure reliability and scalability in real-world scenarios [2]. This study aims to improve the security ofWSNs by providing a robust, adaptive, and energy-efficient IDS solution.