Detection and Mitigation of Vampire Attacks in Wireless Sensor Networks using Weighted Hopfield Neural Network with Nature Inspired Optimal Path Selection

Wireless sensor networks (WSNs) consist of several nodes whose primary objectives are to monitor and regulate surroundings. Furthermore, sensor nodes are distributed in accordance with network utilization. The energy consumption of sensor nodes is one of the most important problems in this kind of network. In fixed-sink networks, nodes in close proximity to the sink serve as intermediaries for transmitting data from other nodes to the sink. This results in a significant decrease in sensor energy usage. Consequently, the network’s lifespan decreases. Due to their vulnerabilities, sensor nodes are prone to various attacks, including the vampire attack that poses a danger to WSN. This paper presents a novel approach for the detection and mitigation of vampire attacks using a Weighted Hopfield Neural Network (WHNN) combined with bio-inspired optimal path selection. The WHNN is employed to detect abnormal energy consumption patterns indicative of vampire attacks, leveraging its associative memory capabilities. To counteract the detected attacks, we incorporate a bio-inspired optimization algorithm, such as Aquila optimizer Algorithm (AOA), to dynamically select the most energy-efficient paths for data transmission. This dual-layered strategy not only enhances the detection accuracy of vampire attacks but also ensures optimal path selection. Due to their vulnerabilities, sensor nodes are prone to various attacks, including the vampire attack that poses a danger to WSN. The superiority of the WHNN-AOA technique is proved by simulation results, when compared to current schemes, based on performance measures such as packet delivery ratio, average throughput, detection ratio, and network lifespan, while also reducing the average residual energy.