Enhancing IoT Security with Dynamic Reputation-Based Trust Management

Abstract

The exponential growth of the Internet of Things (IoT) has raised significant security concerns, particularly in managing trust among heterogeneous devices in dynamic environments. This paper proposes a Dynamic Reputation-Based Trust Management (DRBTM) system to enhance security across IoT networks by leveraging reputation scores to assess and manage device trustworthiness. The DRBTM system dynamically updates trust values based on device behavior and reputation ratings, allowing for adaptive responses to potential threats. Our approach incorporates a reputation update algorithm that evaluates devices based on interaction history, frequency of successful interactions, and observed malicious activities. Experimental results show that DRBTM achieves an 89% accuracy in detecting untrustworthy nodes and a 95% precision in isolating compromised devices, with a response time of 150 ms, making it suitable for real-time IoT applications. In simulations, the proposed model reduced malicious node impact by 72% compared to traditional static trust models, demonstrating a 40% improvement in overall network reliability. The results suggest that DRBTM significantly enhances security in IoT networks by dynamically adjusting trust in real time, ensuring high levels of network resilience and robustness. This reputation-based approach offers a scalable and efficient solution for trust management, suitable for diverse IoT applications, from smart homes to industrial IoT systems.