ENERGY EFFICIENT INTRUSION DETECTION SYSTEM IN WIRELESS SENSOR NETWORKS
B.Bharathi Kannan, S. Srinivasan · INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY · 2023
The extensive use of ZigBee standards in Internet of Things (IoT) networks and wireless sensor networks (WSN) has led to questions about how well-equipped current security measures are to fend off emerging dangers like wormhole attacks and Distributed Denial of Service (DDoS) attacks.While ZigBee was designed with costeffectiveness, security, and network resilience in mind, modern security techniques frequently fall short of offering all-encompassing protection while placing a substantial burden on energy, memory, and computation.The security of ZigBee-based WSNs against wormhole and DDoS assaults is strengthened in this work by the unique method we provide.The Energy Efficient Intrusion Detection System (EE-IDS) and the Energy Efficient Trust System (EE-TS) are the two essential parts of our system.Together, these elements support effective threat detection and mitigation while using the fewest resources possible.We compare the performance of our suggested method to three other routing protocols in order to determine how effective it is: Ad hoc On-Demand Distance Vector (AODV), Shortcut Tree Routing (STR), and Opportunistic Shortcut Tree Routing (OSTR). We get a deeper grasp of the EE-IDS and EE-TS' capabilities under various network dynamics and traffic patterns by deploying them in these scenarios. The proposed Energy Efficient Trust System for Wormhole detection (EE-TSW) and the Energy Efficient Trust System (EE-TS) for DDoS attack detection are thoroughly evaluated through extensive simulations utilising the NS2 (Network Simulator 2) platform. According to the findings of our simulations, the EE-IDS, in particular the versions EE-IDS-AODV, EE-IDS-STR, and EE-IDS-OSTR, consistently beat the EE-TSW in wormhole attack detection. Moreover, our Energy Efficient Intrusion Detection System with Energy Prediction (EE-IDSEP) demonstrates superior performance in detecting DDoS assaults compared to the current EE-TS, as evidenced by key performance metrics including Packet Delivery Ratio (PDR), Average End-to-End Delay, energy consumption, detection rate, average detection time, and False Positive Rate (FPR).