Two-Loop Covert Attacks Against Constant Value Control of Industrial Control Systems

Abstract

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.