Novel Multi-Faceted Technique for Enhancing the Performance of Wireless Sensor Network in Cyber Security Uses

Wireless Sensor Networks (WSNs) in cybersecurity involves the deployment of linked sensors for monitoring and identifying anomalies within physical or cyber circumstances, hence improving threat detection as well as allowing quick reaction measures. WSN have possible limitations in cyber security, including susceptibility toward signals interference, limited lifespan of batteries and susceptibility to hacker attacks. In this research, we proposed a Cluster-aware swarm-intelligent principal component analysis (CAS-PCA) for improving the efficiency of WSN in cyber security. The proposed technique utilizes a medical dataset. Initially, we described the energy model and network model. In addition, we employed a K-Means clustering for the clustering process in the optimization technique. Furthermore, the optimization process was done by using the principal component analysis (PCA) and particle swarm optimization (PSO). As a result, we deployed some metrics for existing and proposed techniques, our proposed algorithm CAS-PCA results are residual energy (332.5), request failure (0.06), delay (47.53) and lifetime (1439). This outcome provides superior results for lifetime and residual energy, as well as provides lesser outcomes for request failure and delay. WSNs improve cyber security by enabling real-time data monitoring, quick threat identification, as well as effective communication. Their incorporation strengthens network defenses, resulting in robust as well as proactive security postures.