Efficient Malicious Node Detection in WSNs: A Lightweight Mechanism Using Multi-Dimensional Dynamic Trust

As Internet of Things (IoT) technology rapidly evolves, Wireless Sensor Networks (WSNs) have become indispensable in applications such as environmental monitoring and precision agriculture. However, their deployment in unattended environments and dynamic communication conditions make them vulnerable to sophisticated attacks, including selective forwarding and masquerading behaviors. Current trust management methods have not well considered the evaluation of dynamic and complex trust evidence under resource constraint. To tackle these issues, in this paper we propose an adaptive trust management mechanism for malicious node detection. The proposed mechanism integrates communication, behavioral, and energy evidence, which captures behavioral patterns via temporal decay and spatial deviation analysis. To address time-varying evidence reliability, a dynamic weight adjustment mechanism based on fuzzy clustering is further developed, which monitors link quality metrics and categorizes trust evidence by environmental sensitivity. This enables the dynamic mechanism to automatically reconfigure evidence weights according to environmental adaptability under dynamic conditions. Extensive experiments demonstrate that our proposed mechanism achieves accuracy above 98% and false positive rate (FPR) below 1% in dynamic environments, outperforming baseline methods by up to 16% in accuracy and 19% in FPR reduction.