Identification of Successive "Unobservable" Cyber Data Attacks in Power Systems Through Matrix Decomposition

TL;DR

This paper introduces a novel convex-optimization framework for detecting successive unobservable cyber data attacks in power systems by exploiting the low-rank structure of PMU data, with proven theoretical guarantees and validated experiments.

Contribution

It formulates the attack detection as a matrix decomposition problem and provides a new method with theoretical guarantees for identifying unobservable cyber attacks.

Findings

Effective detection of unobservable cyber attacks demonstrated on real and synthetic data.

The proposed method outperforms existing techniques in identifying successive cyber attacks.

Theoretical analysis confirms the robustness of the matrix decomposition approach.

Abstract

This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting "unobservable" cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.