TIDS: Trust Value-Based IDS Framework for Wireless Body Area Network

This study aims to develop a Trust Value Based Intrusion Detection System (TIDS) to identify and prevent Denial of Sleep attacks (DoSL) in Wireless Body Sensor Networks (WBAN). To detect and deter DoSL attacks, this IDS employs pre-distributed random keys, random passwords, the trust value of each node, node energy consumption, and an agent database. Since sending and receiving information packets within the network consumes the energy of the sensors, adopting an appropriate and optimal method to reduce energy consumption and efficient routing selection is necessary. The proposed framework uses Genetic Algorithm (GA) to select the optimal Cluster Head (CH) and the Ad-hoc On-demand Distance Vector (AODV) routing protocol for intra-cluster routing. This work simulates two attack scenarios: one in the presence of IDS and one in its absence, to understand the efficiency and effectiveness of IDS. In both scenarios, we measure and compare various network parameters such as throughput, network lifetime, Packet Delivery Rate (PDR), and node residual energy. We also benchmark TIDS against one of the recent highly cited works, “Secure and energy-efficient framework using Internet of Medical Things (IoMT) for e-healthcare (SEF-IoMT)”, against all network parameters listed above. The simulation results and their comparison with the benchmark study show that the proposed method could significantly improve the network parameters in deterring DoSL attacks. This study uses NS2 for running all tests and experiments.