A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert a...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2018-01, Vol.2018 (2018), p.1-14
Main Authors: Li, Weize, Wang, Zhiliang, Xie, Lun
Format: Article
Language:English
Subjects:
Control systems
Controllers
Data integrity
Industrial electronics
Knowledge
Least squares
Methods
Neural networks
Principal components analysis
Sensors
Studies
Support vector machines
Windows (intervals)
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Summary:Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR). By estimating a plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant. Then, the covert agent is used to construct a covert loop, which can keep the controller’s input and output both stealthy over a finite time window. Experiments have been carried out to show the effectiveness of the proposed method.
ISSN:2090-0147
2090-0155
DOI:10.1155/2018/7204939