Data-driven Supervisory Control under Attacks via Spectral Learning

TL;DR

This paper introduces a spectral learning-based approach to model and mitigate broad-spectrum cyber attacks on cyber-physical systems by constructing finite-state transducer models from observed attack behaviors.

Contribution

It presents a novel spectral analysis method to learn attacker models from observed data and synthesizes supervisors to neutralize attacks in CPS.

Findings

Successfully models broad-spectrum attacks using spectral learning.

Enables synthesis of supervisors to mitigate identified attack behaviors.

Improves robustness of supervisory control in cyber-physical systems.

Abstract

The technological advancements facilitating the rapid development of cyber-physical systems (CPS) also render such systems vulnerable to cyber attacks with devastating effects. Supervisory control is a commonly used control method to neutralize attacks on CPS. The supervisor strives to confine the (symbolic) paths of the system to a desired language via sensors and actuators in a closed control loop, even when attackers can manipulate the symbols received by the sensors and actuators. Currently, supervisory control methods face limitations when effectively identifying and mitigating unknown, broad-spectrum attackers. In order to capture the behavior of broad-spectrum attacks on both sensing and actuation channels we model the plant, supervisors, and attackers with finite-state transducers (FSTs). Our general method for addressing unknown attackers involves constructing FST models of the attackers from spectral analysis of their input and output symbol sequences recorded from a history of attack behaviors observed in a supervisory control loop. To construct these FST models, we devise a novel learning method based on the recorded history of attack behaviors. A supervisor is synthesized using such models to neutralize the attacks.