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

arXiv: https://arxiv.org/abs/1607.04776 · HTML: https://mlsanity.com/paper/1607.04776/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2016.

## Built on by (most cited first, 2 of 2)

- [A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems](https://mlsanity.com/paper/2210.06729/) (2022)
- [Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling](https://mlsanity.com/paper/2409.11529/) (2024)

## Abstract (arXiv)

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.