Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2021
Abstract
Cyber-Physical Systems (CPSs) are composed of computational control logic and physical processes, that intertwine with each other. CPSs are widely used in various domains of daily life, including those safety-critical systems and infrastructures, such as medical monitoring, autonomous vehicles, and water treatment systems. It is thus critical to effectively test them. However, it is not easy to obtain test cases which can fail the CPS. In this work, we propose a failure-inducing input generation approach FIGCPS for CPS, which requires no knowledge of the CPS under test or any history logs of the CPS which are usually hard to obtain. Our approach adopts deep reinforcement learning techniques, which interact with the CPS under test and effectively search for failure-inducing input guided by rewards. Our approach adaptively collects information from the CPS, which reduces the training time and is also able to explore different states. Moreover, our approach considers both continuous action space and large-dimension discrete action space, which are common for CPS systems. The evaluation results show that FIGCPS not only achieves a higher success rate than the state-of-the-art approach, but also finds two new attacks in a well-tested CPS.
Keywords
Test Case Generation, CPS, Deep Reinforcement Learning
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE): Australia, November 15-19: Proceedings
First Page
555
Last Page
567
ISBN
9781665403375
Identifier
10.1109/ASE51524.2021.9678832
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
ZHANG, Shaohua; LIU, Shuang; SUN, Jun; CHEN, Yuqi; HUANG, Wenzhi; LIU, Jinyi; LIU, Jian; and HAO, Jianye.
FIGCPS: Effective failure-inducing input generation for cyber-physical systems with deep reinforcement learning. (2021). 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE): Australia, November 15-19: Proceedings. 555-567.
Available at: https://ink.library.smu.edu.sg/sis_research/6221
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ASE51524.2021.9678832