Process Resilience under Optimal Data Injection Attacks

TL;DR

This paper investigates the resilience of process systems against optimal data injection attacks using an information-theoretic approach, proposing methods to assess and enhance system robustness.

Contribution

It introduces a novel framework for designing stealthy, optimal data injection attacks and provides a systematic assessment of process system vulnerabilities.

Findings

The attack design problem is convex under certain conditions.

A greedy algorithm effectively constructs optimal attacks.

Numerical evaluation demonstrates the approach's applicability on a two-reactor process.

Abstract

In this paper, we study the resilience of process systems in an {\it information-theoretic framework}, from the perspective of an attacker capable of optimally constructing data injection attacks. The attack aims to distract the stationary distributions of process variables and stay stealthy, simultaneously. The problem is formulated as designing a multivariate Gaussian distribution to maximize the Kullback-Leibler divergence between the stationary distributions of states and state estimates under attacks and without attacks, while minimizing that between the distributions of sensor measurements. When the attacker has limited access to sensors, sparse attacks are proposed by incorporating a sparsity constraint. {We conduct theoretical analysis on the convexity of the attack construction problem and present a greedy algorithm, which enables systematic assessment of measurement vulnerability, thereby offering insights into the inherent resilience of process systems. We numerically evaluate the performance of proposed constructions on a two-reactor process.