A Secure Robot Learning Framework for Cyber Attack Scheduling and Countermeasure

Chengwei Wu, Weiran Yao, Wensheng Luo, Wei Pan, Guanghui Sun, Hui Xie, Ligang Wu

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)3722-3738
Number of pages17
JournalIEEE Transactions on Robotics
Volume39
Issue number5
DOIs
Publication statusPublished - 5 Jun 2023

Keywords

  • Deep reinforcement learning
  • optimal attack scheduling
  • robot
  • secure control

Fingerprint

Dive into the research topics of 'A Secure Robot Learning Framework for Cyber Attack Scheduling and Countermeasure'. Together they form a unique fingerprint.

Cite this