Hybrid optimization driven deep residual network for attack detection and attack mitigation in wireless sensor networks

Abstract

Wireless sensor network (WSN) comprises small sensor nodes that have the ability of sensing platform, store and send data. In addition, the sensor nodes pose limited resource such as energy, computing power. Clustering is an effective manner for diminishing energy usage utilized by sensor nodes for sending data from source to base station (BS). The multi-path routing is utilized for routing data amongst nodes. However, the WSN are susceptible to several types of security issues which can degrade network performance. WSN are susceptible to various types of attacks, which degraded the performance of entire network. Hence, the primary purpose of this research is to design and develop attack detection and attack mitigation system in WSN for detecting the attackers. This paper devises an energy efficient and optimization aware deep model for attack detection and mitigation in WSN. Here, the first step is the simulation of WSN nodes. The overall procedure of the proposed model includes simulation of WSN, cluster head selection, routing to BS, Sybil attack detection in BS and finally attack mitigation in BS. The first step is WSN simulation and then the selection of energy efficient cluster head is done using Lower Energy Adaptive Clustering Hierarchy (LEACH) protocol.