GUIDE: GAN-based UAV IDS Enhancement

Abstract

With the development of information technology, many devices are connected and automated by networks. Unmanned Areal Vehicles (UAVs), commonly known as drones, are one of the most popular devices that can perform various tasks. However, the risk of cyberattacks on UAVs is increasing as UAV utilization grows. These cyberattacks can cause serious safety problems, such as crashes. Therefore, it is essential to detect these attacks and take countermeasures. As a countermeasure, intrusion detection system (IDS) is widely adopted. To implement IDS for UAVs, it should be lightweight and be able to detect unknown attacks as a requirement. We propose GAN-based UAV IDS Enhancement (GUIDE) to meet the requirements. The GUIDE employs a generative adversarial network (GAN) for integer-valued sequence data augmentation to enhance an IDS’s performance on known and unknown attacks. We used five GANs: SeqGAN, MaskGAN, RankGAN, StepGAN, and LeakGAN; we used four non-learning augmentation methods for the comparative experiment: oversampling, undersampling, noise addition, and random generation. The experimental results demonstrated that the synthetic data generated by GANs improved the detection of known attacks (up to 37 percentage points) and unknown attacks (up to 30 percentage points) while maintaining stable IDS performance. We also analyzed the synthetic data by employing Jensen–Shannon divergence, synthetic ranking agreement, and visualization; we confirmed that the synthetic data contained the characteristics of real data and could be used for training the IDS.