GPS Spoofing Attacks on AI-based Navigation Systems with Obstacle Avoidance in UAV

TL;DR

This paper investigates GPS spoofing vulnerabilities in DRL-based UAV navigation systems, demonstrating that such attacks can compromise both basic and integrated systems, highlighting security concerns in autonomous drone navigation.

Contribution

It models GPS spoofing attacks on DRL-based UAV navigation, combining attack scenarios with real autopilot systems and experimentally demonstrating their feasibility.

Findings

Attacks are possible on basic DRL navigation systems.

Spoofing can compromise integrated DRL and PX4 autopilot systems.

Experimental validation confirms vulnerability to GPS spoofing.

Abstract

Recently, approaches using Deep Reinforcement Learning (DRL) have been proposed to solve UAV navigation systems in complex and unknown environments. However, despite extensive research and attention, systematic studies on various security aspects have not yet been conducted. Therefore, in this paper, we conduct research on security vulnerabilities in DRL-based navigation systems, particularly focusing on GPS spoofing attacks against the system. Many recent basic DRL-based navigation systems fundamentally share an efficient structure. This paper presents an attack model that operates through GPS spoofing attacks briefly modeling the range of spoofing attack against EKF sensor fusion of PX4 autopilot, and combine this with the DRL-based system to design attack scenarios that are closer to reality. Finally, this paper experimentally demonstrated that attacks are possible both in the basic DRL system and in attack models combining the DRL system with PX4 autopilot system.