Detection and mitigation of vampire attacks with secure routing in WSN using weighted RNN and optimal path selection

In Wireless Sensor Networks (WSNs), one of the most significant threats is vampire attacks in sensor nodes. These attacks are marked by malicious behaviors within sensor nodes, often exploiting vulnerabilities inherent in routing protocols. These attacks can disrupt the connectivity of the network and significantly impact the energy resources. However, these intermediate nodes can introduce security vulnerabilities, making network security in WSN is challenging task. To address this issue, a novel deep learning-based vampire attack detection model is proposed. The developed deep learning-based vampire attack detection model is performed by following steps like data collection, attack detection, mitigation, and optimal path selection. Initially, the data attributes for all sensor nodes in the WSN system are collected. Further, the vampire attack detection is carried out by a Weighted Recurrent Neural Network (WRNN), here the weight values are optimized using Enhanced Golf Optimization Algorithm (EGOA). The detected vampire nodes are effectively separated based on different characteristics of nodes like node broadcast count, node energy, and node Packet Received Ratio (PRR). The attack mitigation process is executed by the separation of the vampire nodes from the network, the remaining nodes are considered for the routing process. The optimal paths are chosen by the proposed EGOA. Finally, the result of the suggested vampire attack detection model is compared with the conventional techniques in terms of various evaluation indices.