SRLR: Symbolic Regression based Logic Recovery to Counter Programmable Logic Controller Attacks

TL;DR

SRLR is a symbolic regression-based method that recovers PLC logic from input-output data, providing explainable rules for detecting cyber-attacks in industrial control systems, outperforming existing approaches especially in complex, noisy environments.

Contribution

The paper introduces SRLR, a novel symbolic regression approach tailored for ICS, incorporating domain-specific properties to improve logic recovery and attack detection accuracy.

Findings

SRLR outperforms existing methods by up to 39% in accuracy.

Effective in large-scale, complex ICS environments.

Enhances deep symbolic regression with ICS-specific features.

Abstract

Programmable Logic Controllers (PLCs) are critical components in Industrial Control Systems (ICSs). Their potential exposure to external world makes them susceptible to cyber-attacks. Existing detection methods against controller logic attacks use either specification-based or learnt models. However, specification-based models require experts' manual efforts or access to PLC's source code, while machine learning-based models often fall short of providing explanation for their decisions. We design SRLR -- a it Symbolic Regression based Logic Recovery} solution to identify the logic of a PLC based only on its inputs and outputs. The recovered logic is used to generate explainable rules for detecting controller logic attacks. SRLR enhances the latest deep symbolic regression methods using the following ICS-specific properties: (1) some important ICS control logic is best represented in frequency domain rather than time domain; (2) an ICS controller can operate in multiple modes, each using different logic, where mode switches usually do not happen frequently; (3) a robust controller usually filters out outlier inputs as ICS sensor data can be noisy; and (4) with the above factors captured, the degree of complexity of the formulas is reduced, making effective search possible. Thanks to these enhancements, SRLR consistently outperforms all existing methods in a variety of ICS settings that we evaluate. In terms of the recovery accuracy, SRLR's gain can be as high as 39% in some challenging environment. We also evaluate SRLR on a distribution grid containing hundreds of voltage regulators, demonstrating its stability in handling large-scale, complex systems with varied configurations.