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

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3402842.3407158

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