Enhancing Security for Federated DRL-GAN Strategies in Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are at risk for adversarial attacks, energy inefficiencies, and privacy violations, hence requiring the establishment of a resilient security framework. This study introduces a Federated Deep Reinforcement Learning model integrated with Generative Adversarial Networks (Federated DRL-GAN) to tackle these difficulties. The platform incorporates federated learning (FL) for decentralised data security, deep reinforcement learning (DRL) for adaptive decision-making, and generative adversarial networks (GANs) for adversarial resilience. The suggested system shows enhanced performance relative to conventional FL and DRL models, with an accuracy of 98.2%, precision of 97.5%, recall of 96.9%, and a ROC-AUC score of 98.8%. The model decreases energy usage by 25%, hence improving the lifespan of the sensor network. The Federated DRL-GAN model improves detection capabilities and preserves efficiency, compared to current methods that are vulnerable to model poisoning and adversarial assaults. This study presents a scalable and secure wireless sensor network infrastructure appropriate for the Internet of Things, smart grids, and industrial automation.