@inproceedings{6efaee24226f4901b2303c5448134de3,
title = "ObjSim: Efficient testing of cyber-physical systems",
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",
author = "Jun Sun and Zijiang Yang",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 4th ACM SIGSOFT International Workshop on Testing, Analysis, and Verification of Cyber-Physical Systems and Internet of Things, TAV-CPS/IoT 2020, co-located with the ACM Sigsoft International Conference on Software Testing and Analysis, ISSTA 2020 ; Conference date: 19-07-2020",
year = "2020",
month = jul,
day = "19",
doi = "10.1145/3402842.3407158",
language = "英语",
series = "TAV-CPS/IoT 2020 - Proceedings of the 4th ACM SIGSOFT International Workshop on Testing, Analysis, and Verification of Cyber-Physical Systems and Internet of Things, co-located with ISSTA 2020",
publisher = "Association for Computing Machinery",
pages = "1--2",
editor = "Yan Cai and Tingting Yu",
booktitle = "TAV-CPS/IoT 2020 - Proceedings of the 4th ACM SIGSOFT International Workshop on Testing, Analysis, and Verification of Cyber-Physical Systems and Internet of Things, co-located with ISSTA 2020",
}
