Cyber-Physical Attacks in Power Networks: Models, Fundamental Limitations and Monitor Design

TL;DR

This paper develops a unified framework and advanced monitoring methods to detect and identify cyber-physical attacks in power networks, addressing fundamental limitations and improving detection capabilities.

Contribution

It introduces a generalized attack model for power systems and designs provably-correct dynamic detection and identification procedures using geometric control theory.

Findings

The proposed methods outperform existing static detection algorithms.

Fundamental limits of attack detection are characterized.

Numerical studies validate the effectiveness of the new procedures.

Abstract

Future power networks will be characterized by safe and reliable functionality against physical malfunctions and cyber attacks. This paper proposes a unified framework and advanced monitoring procedures to detect and identify network components malfunction or measurements corruption caused by an omniscient adversary. We model a power system under cyber-physical attack as a linear time-invariant descriptor system with unknown inputs. Our attack model generalizes the prototypical stealth, (dynamic) false-data injection and replay attacks. We characterize the fundamental limitations of both static and dynamic procedures for attack detection and identification. Additionally, we design provably-correct (dynamic) detection and identification procedures based on tools from geometric control theory. Finally, we illustrate the effectiveness of our method through a comparison with existing (static) detection algorithms, and through a numerical study.