Generative Adversarial Networks for Synthetic Jamming Attacks on UAVs

Intrusion Detection Systems (IDS) have been significant for Unmanned Aerial Vehicles (UAVs) since high connectivity is essential for such vehicles. Recently, machine learning-based defense mechanisms have contributed to detecting intrusions. Data are crucial to better results with machine learning algorithms. However, security data is not freely distributed so there is a lack of security-related data about UAVs that can be processed with machine learning. In this research, we create synthetic security data for UAVs using Generative Adversarial Networks (GANs) that can be utilized to develop machine learning-based IDS systems. We propose employing Conditional Tabular Generative Adversarial Networks (CTGANs), Gaussian copulas, and Variational AutoEncoders (VAEs) to generate syn-thetic jamming attacks from the widely-used UAV attack dataset. We experimentally evaluated the performance of each model by using many metrics, such as Kullback-Leibler decomposition. Experimental results show that the distribution of CTGAN synthetic data is similar to the distribution of real data. Finally, we created a machine learning-based IDS and applied it to each dataset to validate the success rate of intrusion detection with newly generated synthetic data. It was observed that CTGAN-generated data decreased the IDS accuracy by approximately 25%, while Gaussian copula-generated data resulted in a 20% decrease, and VAE-generated data led to a 1% reduction, all relative to the accuracy achieved with real data. These findings suggest that synthetic data generation holds significant potential for enhancing the security frameworks of UAVs. The experimental results demonstrate that the CTGAN model is particularly effective in producing synthetic data that challenges traditional intrusion detection mechanisms, thereby contributing to the development of more robust IDS capabilities.