Yaghoubi, Mohammad (2023) TIDS: Trust Value-Based IDS Framework for Wireless Body Area Network. Research Master thesis, Victoria University.

Abstract

This study aims to develop an Intrusion Detection System (IDS) to identify and prevent Denial of Sleep attacks (DoSL) in Wireless Body Area Networks (WBAN). In a DoSL attack, the attacker sends malicious and false information packets, keeping the sensors implanted in the patient's body active for a long time, resulting in quick battery drainage and a reduction in network efficiency. To prevent this attack (DoSL), this study employs pre-distributed random keys, random passwords, the trust value of each node, node energy consumption, the IDS, and an IDS database. The IDS plays a critical role in detecting and preventing DoSL attacks by monitoring the network traffic received and sent between nodes and analyzing data from the IDS database. Moreover, the IDS database is responsible for recording and archiving the events of the WBAN, the number of packets sent and received between nodes, and reporting the recorded information to the IDS. Based on the data from the database and continuous monitoring of the network traffic received and sent between the nodes, the mentioned IDS can detect, prevent, and remove malicious DoSL packets and nodes from the WBAN. WBANs are an advanced technology in medical and therapeutic care where the sensors distributed on the patient's body collect and send the patient's vital information in real-time. Since sending and receiving information packets and traversing the path within the network consumes the sensors' energy, recharging the WBAN sensors is almost impractical and uneconomical. Adopting an appropriate and optimal method to reduce energy consumption and selecting efficient routing is necessary. To tackle this issue, the metaheuristic Artificial Intelligence (AI) mechanism and modified Genetic Algorithm (GA) have been used for clustering and selecting the optimal Cluster Head (CH) based on maximum residual energy and minimum distance between nodes. Furthermore, the modified Ad-hoc On-demand Distance Vector (AODV) routing protocol, which relies on demand, has been used for intra-cluster routing. To evaluate the effectiveness of the proposed IDS in detecting and preventing attacks, we conducted simulations both with and without the presence of IDS and compared various network parameters such as throughput, network lifetime, Packet Delivery Rate (PDR), and node residual energy. We also benchmarked the proposed method against a sample case, the "Secure and energy-efficient framework using Internet of Medical Things (IoMT) for e-healthcare (SEF-IoMT)." Our simulation results showed that the proposed method, which integrated a combination of normalization, intelligent clustering, demand-based routing, and intelligent intrusion detection, enhanced network parameters such as PDR (12%), end-to-end delay (11%), throughput (11%), and energy consumption (11%). It is worth noting that all experiments were conducted using the NS2 simulator.

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