Multi-objective-trust aware improved grey wolf optimization technique for uncovering adversarial attacks in WSNs

Abstract

Wireless sensor network (WSN) is made of several sensor nodes (SN) that monitor various applications and collect environmental data. WSNs are essential for a wide range application, including healthcare, industrial automation, and environmental monitoring. However, these networks are susceptible to several security threats, underscoring the need for robust attack detection systems. Therefore, in this study, a multi-objective-trust aware improved grey wolf optimization (M-TAIGWO) is implemented to mitigate various attacks types. This implemented M-TAIGWO method is used to select secure cluster heads (CH) and routes to obtain secure communication through the network. The implemented M-TAIGWO provides improved security against malicious attacks by increasing the energy efficiency. The important aim of M-TAIGWO is to attain secured data transmission and maximize the WSN network lifetime. The M-TAIGWO method’s performance is evaluated through energy consumption and delay. The implemented method obtains a high PDR of 98% for 500 nodes, which is superior to the quantum behavior and gaussian mutation Archimedes optimization algorithm (QGAOA), with a delay of 15 ms for 100 nodes which is lesser than fuzzy and secured clustering algorithms. In comparison to the trust-based routing protocol for WSNs utilizing an adaptive genetic algorithm (TAGA), this implemented method achieves defense hello fold, black hole, sinkhole, and selective forwarding attacks effectively.