Swarm Intellect Optimization Technique (SIOT) based mischievous detection and improve Authentication in Mobile Wireless Sensor Network

Abstract

Mobile Wireless Sensor Network (MWSN) typically created with no major infrastructure. Thus, they are moderately defenseless to various mischievous attacks; as a result, attack detection is a significant problem in MWSN. The cryptographic technique can avoid several attacks. A mischievous node can simply disrupt a route in the communication path. However, an attacker captures sensor nodes, evokes their cryptanalytic key, and modifies their code to behave mischievously. Hence, the mischievous node detection is a significant problem in the network. To solve these problems, in this paper, Swarm Intellect Optimization Technique based mischievous detection and improve Authentication in MWSN. In this approach, the minimum distance nodes are formed the clusters. Then cluster Heads (CHs) are selected based on the node weight. Observing forwarding behavior, Observing Great energy Communication and Observing fake route ads parameters are decided the MWSN Mischievous nodes. The presented approach is validated by a network simulator. Simulation results shows the SIOT approach detect the mischievous nodes efficiently.