A Secure Robot Learning Framework for Cyber Attack Scheduling and Countermeasure

A Secure Robot Learning Framework for Cyber Attack Scheduling and Countermeasure

Wu, Chengwei (Harbin Institute of Technology)
Yao, Weiran (Harbin Institute of Technology)
Luo, Wensheng (Harbin Institute of Technology)
Pan, W. (TU Delft Robot Dynamics; The University of Manchester)
Sun, Guanghui (Harbin Institute of Technology)
Xie, Hui (Harbin Institute of Technology)
Wu, Ligang (Harbin Institute of Technology)

2023

The problem of learning-based control for robots has been extensively studied, whereas the security issue under malicious adversaries has not been paid much attention to. Malicious adversaries can invade intelligent devices and communication networks used in robots, causing incidents, achieving illegal objectives, and even injuring people. This article first investigates the problems of optimal false data injection attack scheduling and countermeasure design for car-like robots in the framework of deep reinforcement learning. Using a state-of-the-art deep reinforcement learning approach, an optimal false data injection attack scheme is proposed to deteriorate the tracking performance of a robot, guaranteeing the tradeoff between the attack efficiency and the limited attack energy. Then, an optimal tracking control strategy is learned to mitigate attacks and recover the tracking performance. More importantly, a theoretical stability guarantee of a robot using the learning-based secure control scheme is achieved. Both simulated and real-world experiments are conducted to show the effectiveness of the proposed schemes.

Deep reinforcement learning
optimal attack scheduling
robot
secure control

http://resolver.tudelft.nl/uuid:9a59a549-339f-4469-86d7-3b7ed60e8b48

https://doi.org/10.1109/TRO.2023.3275875

2023-12-05

1552-3098

IEEE Transactions on Robotics, 39 (5), 3722-3738

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Chengwei Wu, Weiran Yao, Wensheng Luo, W. Pan, Guanghui Sun, Hui Xie, Ligang Wu