Enhancing the security of unmanned aerial systems using digital-twin technology and intrusion detection

In this paper the general susceptibilities of Unmanned Aerial Vehicles (UAVs) against modern-cyber threats are explored and potential solutions proposed. This is achieved by applying digital-twin architectures and data-driven methods to UAVs to facilitate identification of real-time intrusions and anomalies. These concepts are validated by performing novelty detection on open access UAV flight data with GPS spoofing attacks, which represents a typical system use-case. Multiple machine learning models are trained to demonstrate the feasibility of detecting modern cyber-intrusions and anomalies using the digital-twin architecture. This includes both classical and deep learning techniques to help identify the most suitable model types for the proposed design. The overall results are positive and help highlight the potential of digital-twin architectures for the UAV contexts.