Trust-Based Intrusion Detection in Wireless Sensor Networks

We propose a trust-based intrusion detection scheme utilizing a highly scalable hierarchical trust management protocol for clustered wireless sensor networks. Unlike existing work, we consider a trust metric considering both quality of service (QoS) trust and social trust for detecting malicious nodes. By statistically analyzing peer-to-peer trust evaluation results collected from sensor nodes, each cluster head applies trust-based intrusion detection to assess the trustworthiness and maliciousness of sensor nodes in its cluster. Cluster heads themselves are evaluated by the base station. We develop an analytical model based on stochastic Petri nets for performance evaluation of the proposed trust-based intrusion detection scheme, as well as a statistical method for calculating the false alarm probability. We analyze the sensitivity of false alarms with respect to the minimum trust threshold below which a node is considered malicious. Our results show that there exists an optimal trust threshold for minimizing false positives and false negatives. Further, the optimal trust threshold differs depending on the anticipated wireless sensor network lifetime.