FGA-IDS: A Federated Learning and GAN-Augmented Intrusion Detection System for UAV Networks

The rapid expansion of Unmanned Aerial Vehicle (UAV) networks introduces significant cybersecurity challenges, necessitating robust and adaptive intrusion detection systems that operate efficiently under resource and privacy constraints. Existing solutions often struggle with privacy concerns, limited computational resources, and insufficient training data. To address these challenges, we have proposed the Federated GAN-Augmented Intrusion Detection System (FGA-IDS), which integrates Generative Adversarial Networks (GANs), Federated Learning (FL), machine learning algorithms, and differential privacy techniques to enable collaborative model training across distributed UAVs without compromising sensitive data. GANs augment local datasets with synthetic data, mitigating data scarcity and imbalance. Experimental results demonstrate that FGA-IDS significantly enhances intrusion detection accuracy and reliability in UAV networks, achieves efficient training times suitable for dynamic environments, and reduces bandwidth usage by approximately 99.65% compared to centralized approaches. By balancing enhanced detection capabilities with privacy protection, computational efficiency, and bandwidth reduction, FGA-IDS offers a privacy-preserving, decentralized, and resilient security solution for UAV networks.