Anomaly detection using isomorphic analysis for false data injection attacks in industrial control systems

Zhang, Xinchen1,2,3; Jiang, Zhihan1; Ding, Yulong2,3; Ngai, Edith C. H.1; Yang, Shuang-Hua2,3,4

2024-09-01

10.1016/j.jfranklin.2024.107000

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS   影响因子和分区

0016-0032

1879-2693

As the Industrial Internet-of-Things (IIoT) evolves, a growing number of industrial control systems (ICSs) are connecting to the Internet, making them more vulnerable to malicious attacks. This paper addresses the detection of false data injection (FDI) attacks, a prevalent threat to open ICSs. We introduce an innovative anomaly detection technique using isomorphic analysis to safeguard ICSs against FDI attacks. Isomorphic analysis involves comparing transmitted signals with their expected values, which are derived from mathematical models or isomorphic components. For a comprehensive defense mechanism, we incorporate three specific detectors: the control signal detector, the actuating signal detector, and the sensor reading detector. Designed to detect FDI attacks across various parts of the ICS, these detectors ensure the integrity of all transmitted signals throughout the physical control system. While the control signal detector adopts a threshold method, the other two rely on statistical approaches. If an attack is detected, the detectors can correct tampered signals before they reach downstream components, enhancing the system's overall resilience and fault tolerance. The effectiveness of these detectors is supported by rigorous mathematical proofs. Moreover, our experimental findings further reveal the superiority of the isomorphic strategy over prior work in terms of detection rate, detection time delay, and system resilience.

Anomaly detection False-data injection attacks Industrial control systems Secure control

SCI ; EI

英语

通讯

National Natural Science Foundation of China["92067109","61873119","62211530106"] ; Shenzhen Science and Technology Program, China["ZDSYS20210623092007023","GJHZ20210705141808024"] ; Educational Commission of Guangdong Province, China[2019KZDZX1018] ; UGC General Research Fund from Hong Kong["17203320","17209822"]

Automation & Control Systems ; Engineering ; Mathematics

Automation & Control Systems ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic ; Mathematics, Interdisciplinary Applications

WOS:001265936300001

PERGAMON-ELSEVIER SCIENCE LTD

20242716647686

Delay control systems ; Fault tolerance ; Network security ; Signal detection

Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Control Systems:731.1

ENGINEERING

Web of Science

1.Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Key Lab Safety & Secur Next Generat Ind I, Shenzhen, Peoples R China
3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China
4.Univ Reading, Dept Comp Sci, Reading, England

Zhang, Xinchen,Jiang, Zhihan,Ding, Yulong,et al. Anomaly detection using isomorphic analysis for false data injection attacks in industrial control systems[J]. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS,2024,361(13).

Zhang, Xinchen,Jiang, Zhihan,Ding, Yulong,Ngai, Edith C. H.,&Yang, Shuang-Hua.(2024).Anomaly detection using isomorphic analysis for false data injection attacks in industrial control systems.JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS,361(13).

Zhang, Xinchen,et al."Anomaly detection using isomorphic analysis for false data injection attacks in industrial control systems".JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 361.13(2024).