Realtime Intrusion Detection In Unmanned Aerial Vehicles Using Active Learning and Generative Adversarial Networks

This paper introduces a novel real-time intrusion detection approach for UAV networks, addressing current security challenges. We leverage Generative Adversarial Networks (GANs) to generate synthetic data and implement a stream-based active learning process for prompt detection and response. Our method merges GANs with active learning, involving human experts to navigate the dynamic nature of UAV data and improve detection accuracy. Our results show that this system outperforms traditional intrusion detection methods, promising a new direction for future security frameworks and their application across various networked systems.