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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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 |
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cyber-physical system fuzzing machine learning network testing Software Engineering SUN, Jun YANG, Zijiang ObjSim: Efficient testing of cyber-physical systems |
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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. |
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SUN, Jun YANG, Zijiang |
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SUN, Jun YANG, Zijiang |
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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 |
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ObjSim: Efficient testing of cyber-physical systems |
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ObjSim: Efficient testing of cyber-physical systems |
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objsim: efficient testing of cyber-physical systems |
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Institutional Knowledge at Singapore Management University |
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2020 |
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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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