ObjSim: Efficient testing of cyber-physical systems

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...

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Main Authors: SUN, Jun, YANG, Zijiang
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5959
https://ink.library.smu.edu.sg/context/sis_research/article/6962/viewcontent/3402842.3407158.pdf
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spelling sg-smu-ink.sis_research-69622021-05-24T07:42:51Z ObjSim: Efficient testing of cyber-physical systems SUN, Jun YANG, Zijiang 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. 2020-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5959 info:doi/10.1145/3402842.3407158 https://ink.library.smu.edu.sg/context/sis_research/article/6962/viewcontent/3402842.3407158.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University cyber-physical system fuzzing machine learning network testing Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic cyber-physical system
fuzzing
machine learning
network
testing
Software Engineering
spellingShingle cyber-physical system
fuzzing
machine learning
network
testing
Software Engineering
SUN, Jun
YANG, Zijiang
ObjSim: Efficient testing of cyber-physical systems
description 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.
format text
author SUN, Jun
YANG, Zijiang
author_facet SUN, Jun
YANG, Zijiang
author_sort SUN, Jun
title ObjSim: Efficient testing of cyber-physical systems
title_short ObjSim: Efficient testing of cyber-physical systems
title_full ObjSim: Efficient testing of cyber-physical systems
title_fullStr ObjSim: Efficient testing of cyber-physical systems
title_full_unstemmed ObjSim: Efficient testing of cyber-physical systems
title_sort objsim: efficient testing of cyber-physical systems
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5959
https://ink.library.smu.edu.sg/context/sis_research/article/6962/viewcontent/3402842.3407158.pdf
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