Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 2, Pages 663-676Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2819677
Keywords
Covert attacks; data integrity (DI) attacks; industrial control systems (ICSs); programmable logic controllers (PLCs); support vector machines (SVMs)
Categories
Funding
- National Key Research and Development Program [2016 YFB1001404]
- National Natural Science Foundation of China [61672093, 61432004]
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In the field of covert data integrity attacks, considerable attention has focused on two important issues. One is the issue of how to change the state of a plant, and the other is how to avoid being detected by anomaly detectors. A two-loop covert attack is presented to provide an integrated solution for these two issues. As an exploratory attempt to establish the feasibility of machine learning-based covert attacks, it applies the least squares support vector machine to constructing covert attacks. The proposed attack consists of an attack loop and a covert loop, which are based on an attack agent and a covert agent, respectively. The attack agent can move the steady state of a target plant to a desired state, and the covert agent can closely imitate the normal steady state of the plant to cover up the attack agent. In particular, the attack is directed to proportional-integral-derivative algorithms. Experiments are carried out to demonstrate the feasibility of the proposed attack and show the applicability of machine learning methods in constructing covert attacks.
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