Performance Evaluation of Malicious Node Detection and Mitigation of IoT-Based Trust Model for Wireless Sensor Network

The Internet of Things (IoT) enables seamless real-time monitoring and data exchange across distributed and heterogeneous environments with wireless sensor networks (WSNs). The open architecture and resource constraints of wireless sensor networks (WSNs) make them highly vulnerable to internal security threats caused by malicious or compromised nodes, particularly in Internet of Things (IoT) environments. To address this issue, we proposed Dynamic Trust Evaluation Model (DTEM), designed to provide a secure, scalable, and efficient framework for IoT-based WSNs. The proposed model identifies the role of trust management in routing, data aggregation, and intrusion detection, including trust-based protocols. DTEM incorporates a lightweight elliptic curve cryptography (ECC) mechanism to ensure secure communication, protect trust information from manipulation, and enhance overall system reliability. In addition, machine learning techniques are employed to improve malicious node classification accuracy. Component-wise analysis demonstrates that the dynamic trust evaluation forms the core detection mechanism, while ECC enhances communication security and machine learning improves malicious node classification accuracy. A large-scale network simulation is conducted to evaluate DTEM’s performance under various attack scenarios. Results demonstrate improved malicious node detection accuracy, higher packet delivery ratios, reduced energy consumption, and lower communication overheads. The proposed DTEM framework proves to be a robust and scalable solution for securing IoT-based wireless sensor networks, making it suitable for real-world applications.