Enhancing UAV Network Security: A Human-in-the-Loop and GAN-Based Approach to Intrusion Detection

Uncrewed aerial vehicles (UAVs) are becoming essential in various sectors, such as commercial delivery, agricultural monitoring, and disaster response. Despite their benefits, the rapid adoption of UAVs poses substantial security challenges, especially in drone network intrusion detection. Traditional intrusion detection datasets often suffer from limitations like small sample sizes and uneven distribution, undermining the effectiveness of intrusion detection systems. Moreover, conventional machine learning (ML) approaches generally require extensive, well-labeled datasets that are expensive and labor intensive to produce. To overcome these challenges, we introduce a generative adversarial network (GAN) model designed to enhance and balance the limited datasets available for drone networks. This model significantly improves data quality and quantity, thus optimizing the training process for intrusion detection models. Furthermore, we propose a Human-in-the-Loop (HITL) ML framework that integrates human expertise to guide the learning process and mitigate the costs of labeling. Our comprehensive evaluation demonstrates that the combined application of the GAN model and the HITL framework significantly outperforms traditional baseline models. This approach not only achieves an intrusion detection accuracy of up to 99% across various experimental datasets but also dramatically reduces the requirement for large amounts of labeled data by up to 98%, providing a cost-effective solution for enhancing UAV network security.