DroneDefGANt: A Generative AI-Based Approach for Detecting UAS Attacks and Faults

Recently, Unmanned Aerial Systems (UAS) have become heavily reliant on communication, navigation, and other critical components such as sensors and actuators, which are essential for operations in both civilian and defense applications. However, this increasing reliance makes UAS more vulnerable to attacks and faults, posing rising threats. While there have been many advances in UAS security, a significant number of studies have proposed artificial intelligence (AI)-enhanced solutions to address these challenges. Yet, no research has explored the potential of generative AI (GenAI) in this domain. GenAI stands out due to its ability to detect and prevent cyberattacks by continuously learning and adapting to new threats and vulnerabilities. In this paper, we propose DroneDefGANt, a GenAI-based approach that combines the capabilities of generative adversarial networks (GAN) and transformer models. The DroneDefGANt is designed to detect both external UAS attacks like GPS spoofing and jamming, and internal attacks such as actuator faults. Through evaluations using synthetic datasets, DroneDefGANt surpassed various conventional AI models, demonstrating superior accuracy and robustness, particularly in the presence of Gaussian noise.