A real-time, lightweight onboard security framework for UAVs against GPS spoofing and unauthorized command access

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in mission-critical operations such as military reconnaissance, disaster relief, and border surveillance. However, they remain vulnerable to cyber-physical threats, particularly unauthorized command injections and GPS spoofing, which can redirect, hijack, or even weaponize drones against their original operators. This study presents a lightweight, fully onboard security framework for UAVs that integrates real-time authentication gating, location-bound AES encryption, and velocity-consistency-based spoofing detection. In the proposed framework, mode change requests originating from remote controllers, ground stations, or companion computers are intercepted and cryptographically verified using a password-based authentication mechanism. The encryption key is dynamically derived from the drone’s home GPS coordinates, ensuring that access remains strictly limited to authorized users operating within the intended mission context. In the event of GPS spoofing, the system utilizes onboard velocity logs to detect inconsistencies between expected and observed motion patterns. Upon detection, the UAV retraces its outbound trajectory using inertial estimation, enabling reliable GPS-independent recovery. Experimental validation was conducted over 10 repeated flight trials under varying environmental conditions. The results demonstrate near-perfect authentication performance, with an AES decryption success rate of 99.3%, successful rejection of 9–10 out of 10 invalid password attempts, and accurate location recovery with an average deviation of 5.8 m and a maximum deviation of 8 m. Unlike cloud-dependent security architectures, the proposed solution operates entirely onboard, requires no external infrastructure, and introduces minimal computational overhead (approximately 5–6% CPU utilization), making it highly suitable for deployment in stealth and communication-denied environments. By integrating access control, encryption, and autonomous fallback navigation into a unified onboard framework, this work advances UAV cybersecurity from a reactive mitigation strategy to a proactive, design-level paradigm embedded within autonomous flight systems. Sustainable Development Goals (SDGs): This work contributes to SDG 9 (Industry, Innovation, and Infrastructure) by advancing resilient and secure autonomous systems, SDG 11 (Sustainable Cities and Communities) through safer deployment of UAVs in urban monitoring and disaster response, and SDG 16 (Peace, Justice, and Strong Institutions) by enhancing security, surveillance integrity, and protection against misuse of aerial technologies.