Deepnoise: Learning sensor and process noise to detect data integrity attacks in CPS

• Published in: China communications Vol. 18; no. 9; pp. 192 - 209
• Main Authors: Luo, Yuan, Cheng, Long, Liang, Yu, Fu, Jianming, Peng, Guojun
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.09.2021; Key Laboratory of Aerospace Information Security and Trusted Computing,Ministry of Education,Wuhan 430072,China%School of Computing,Clemson University,USA%Tencent Technology Shenzhen Company,Shenzhen,China; School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China
• Subjects: Anomaly detection; cyber-physical systems; Data integrity; data integrity attacks; Data models; Detectors; Noise measurement; Robot sensing systems; Sparse matrices; cyber-physical systems; anomaly detec-tion; data integrity attacks

Cyber-physical systems (CPS) have been widely deployed in critical infrastructures and are vulnerable to various attacks. Data integrity attacks manipulate sensor measurements and cause control systems to fail, which are one of the prominent threats to CPS. Anomaly detection methods are proposed to...