Intelligent Intrusion Detection in Clustered Wireless Sensor Networks: A Dynamic Clustering and Machine Learning-Based Approach

Traditional Intrusion Detection Systems (IDS) designed for more conventional network infrastructures are often ill-equipped to handle the unique challenges WSNs pose, leading to significant gaps in security and resilience. This paper introduces an Intelligent Intrusion Detection System (IIDS) explicitly tailored for clustered WSNs to address these critical challenges. The proposed IIDS integrates dynamic clustering with advanced machine learning algorithms to create a robust and adaptive security solution capable of real-time threat detection and mitigation. The dynamic clustering mechanism is designed to continuously monitor and respond to changes in sensor node network topology and energy levels, ensuring that energy consumption is evenly distributed across the network. This approach prolongs the network's operational lifespan and enhances its resilience against attacks by minimising single points of failure. The IDS employs a set of machine learning algorithms, including decision trees, support vector machines, and deep learning models, to accurately identify and classify potential intrusions. The system is engineered to detect a broad spectrum of attacks, ranging from common threats such as DoS and Sybil attacks to more sophisticated and emerging threats, including advanced persistent threats (APTs) and zero-day exploits. By leveraging machine learning, the IIDS can continuously learn from new data, improve its detection capabilities over time, and adapt to the evolving threat landscape. Extensive simulations were conducted to evaluate the proposed IIDS's performance under various network conditions and attack scenarios. The results demonstrate that the IIDS consistently outperforms traditional IDS approaches in several key metrics, including detection accuracy, false positive rate, and energy efficiency.