Leveraging Human-In-The-Loop Machine Learning and GAN-Synthesized Data for Intrusion Detection in Unmanned Aerial Vehicle Networks

The emergence of Unmanned Aerial Vehicle (UAV) networks has brought about intricate security challenges, especially in the domain of intrusion detection. At the same time, traditional machine learning algorithms for network security are becoming progressively inadequate, particularly when confronted with adversarial attacks and advanced persistent threats. Adding to the complexity, real-world UAV data streams are relentless, extensive, and demand substantial storage, making the storage of all data almost unfeasible. Furthermore, a significant gap in research lies in the lack of universally recognized datasets specifically designed for UAV network intrusion detection. Taking into account the aforementioned factors, this paper introduces an innovative approach for real-time intrusion detection. This approach harnesses Human-in-the-Loop Machine Learning (HITL-ML) to integrate human expertise directly into the machine learning process. By doing so, it enhances the system's capacity to adapt to evolving threats. Additionally, we introduce Generative Adversarial Networks (GANs) to synthetically generate data that mimics authentic network intrusions, thereby addressing the issue of limited dataset availability. This integration substantially enhances detection accuracy and reduces false positives, signifying a remarkable leap forward in contrast to conventional detection systems.