Adaptive Trust Threshold Strategy for Misbehaving Node Detection and Isolation

Due to dynamic network topology, distributed architecture and absence of a centralized authority, mobile ad hoc networks (MANETs) are vulnerable to various attacks from misbehaving nodes. To enhance security, various trust-based schemes have been proposed that augment traditional cryptography-based security schemes. However, most of them use static and predefined trust thresholds for node misbehavior detection, without taking into consideration the network conditions locally at each node. Using static thresholds for misbehavior detection may result in high false positives, low malicious node detection rate, and network partitioning. In this paper, we propose a novel Adaptive Trust Threshold (ATT) computation strategy, that adapts the trust threshold in the routing protocol according to network conditions such as rate of link changes, node degree and connectivity, and average neighborhood trustworthiness. We identify the topology factors that affect the trust threshold at each node, and leverage them to build a mathematical model for ATT computation. Our simulation results indicate that the ATT strategy achieves significant improvements in packet delivery ratio, reduction in false positives, and increase in detection rate as compared to traditional static threshold strategies.