Secure Control Systems for Autonomous Quadrotors against Cyber-Attacks

TL;DR

This paper addresses security vulnerabilities in autonomous quadrotors by designing an intelligent control system, developing attack and defense strategies using deep learning, and demonstrating effectiveness through real-world experiments.

Contribution

It introduces the first deployment of reinforcement learning-based security measures on the Agilicious quadrotor platform, including attack schemes and countermeasures, with comprehensive reproducibility resources.

Findings

Deep learning-based attack scheme effectively deteriorates quadrotor tracking.

Reinforcement learning-based control mitigates attacks and restores performance.

Experimental results validate the proposed security frameworks.

Abstract

The problem of safety for robotic systems has been extensively studied. However, little attention has been given to security issues for three-dimensional systems, such as quadrotors. Malicious adversaries can compromise robot sensors and communication networks, causing incidents, achieving illegal objectives, or even injuring people. This study first designs an intelligent control system for autonomous quadrotors. Then, it investigates the problems of optimal false data injection attack scheduling and countermeasure design for unmanned aerial vehicles. Using a state-of-the-art deep learning-based approach, an optimal false data injection attack scheme is proposed to deteriorate a quadrotor's tracking performance with limited attack energy. Subsequently, an optimal tracking control strategy is learned to mitigate attacks and recover the quadrotor's tracking performance. We base our work on Agilicious, a state-of-the-art quadrotor recently deployed for autonomous settings. This paper is the first in the United Kingdom to deploy this quadrotor and implement reinforcement learning on its platform. Therefore, to promote easy reproducibility with minimal engineering overhead, we further provide (1) a comprehensive breakdown of this quadrotor, including software stacks and hardware alternatives; (2) a detailed reinforcement-learning framework to train autonomous controllers on Agilicious agents; and (3) a new open-source environment that builds upon PyFlyt for future reinforcement learning research on Agilicious platforms. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed frameworks in section 5.2.