Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2020
Abstract
Cyber-physical systems (CPSs) play a critical role in automating public infrastructure and thus attract wide range of attacks. Assessing the effectiveness of defense mechanisms is challenging as realistic sets of attacks to test them against are not always available. In this short paper, we briefly describe smart fuzzing, an automated, machine learning guided technique for systematically producing test suites of CPS network attacks. Our approach uses predictive ma- chine learning models and meta-heuristic search algorithms to guide the fuzzing of actuators so as to drive the CPS into different unsafe physical states. The approach has been proven effective on two real-world CPS testbeds.
Keywords
cyber-physical system, fuzzing, machine learning, network, testing
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
TAV-CPS/IoT '20: Proceedings of the 4th ACM SIGSOFT International Workshop on Testing, Analysis, and Verification of Cyber-Physical Systems and Internet of Things, Virtual, July 19
First Page
1
Last Page
2
ISBN
9781450380324
Identifier
10.1145/3402842.3407158
Publisher
ACM
City or Country
New York
Embargo Period
5-24-2021
Citation
SUN, Jun and YANG, Zijiang.
ObjSim: Efficient testing of cyber-physical systems. (2020). TAV-CPS/IoT '20: Proceedings of the 4th ACM SIGSOFT International Workshop on Testing, Analysis, and Verification of Cyber-Physical Systems and Internet of Things, Virtual, July 19. 1-2.
Available at: https://ink.library.smu.edu.sg/sis_research/5959
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/10.1145/3402842.3407158