Low Latency Detection of Sparse False Data Injections in Smart Grids
Abstract
We study low-latency detections of sparse false data injection attacks in power grids, where an adversary can maliciously manipulate power grid operations by modifying measurements at a small number of smart meters. When a power grid is under attack, the detection delay, which is defined as the time difference between the occurrence and detection of the attack, is critical to power grid operations. A shorter detection delay can shorten the response time, thus prevent catastrophic impacts from the attack. The objective of this paper is to develop low-latency false data detection algorithms that can minimize the detection delay subject to constraints on false alarm probability. The false data injection can be modeled with a sparse attack vector, with each non-zero element corresponding to one meter under attack. Since neither the support nor the values of the sparse attack vector is known, a new orthogonal matching pursuit cumulative sum (OMP-CUSUM) algorithm is proposed to identify the meters under attack while minimizing the detection delay. In order to recover the support of the sparse vector, we develop a new stopping condition for the iterative OMP algorithm by analyzing the statistical properties of the power grid measurements. Theoretical analysis and simulation results showmore »
- Authors:
- Publication Date:
- Research Org.:
- Univ. of Arkansas, Little Rock, AR (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1482403
- Alternate Identifier(s):
- OSTI ID: 1482404; OSTI ID: 1511445
- Grant/Contract Number:
- OE0000779; ECCS-1405403; ECCS-1711087
- Resource Type:
- Published Article
- Journal Name:
- IEEE Access
- Additional Journal Information:
- Journal Name: IEEE Access Journal Volume: 6; Journal ID: ISSN 2169-3536
- Publisher:
- Institute of Electrical and Electronics Engineers
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 24 POWER TRANSMISSION AND DISTRIBUTION; Low latency detection; orthogonal matching pursuit; false data injection; cumulative sum
Citation Formats
Akingeneye, Israel, and Wu, Jingxian. Low Latency Detection of Sparse False Data Injections in Smart Grids. United States: N. p., 2018.
Web. doi:10.1109/ACCESS.2018.2873981.
Akingeneye, Israel, & Wu, Jingxian. Low Latency Detection of Sparse False Data Injections in Smart Grids. United States. https://doi.org/10.1109/ACCESS.2018.2873981
Akingeneye, Israel, and Wu, Jingxian. Mon .
"Low Latency Detection of Sparse False Data Injections in Smart Grids". United States. https://doi.org/10.1109/ACCESS.2018.2873981.
@article{osti_1482403,
title = {Low Latency Detection of Sparse False Data Injections in Smart Grids},
author = {Akingeneye, Israel and Wu, Jingxian},
abstractNote = {We study low-latency detections of sparse false data injection attacks in power grids, where an adversary can maliciously manipulate power grid operations by modifying measurements at a small number of smart meters. When a power grid is under attack, the detection delay, which is defined as the time difference between the occurrence and detection of the attack, is critical to power grid operations. A shorter detection delay can shorten the response time, thus prevent catastrophic impacts from the attack. The objective of this paper is to develop low-latency false data detection algorithms that can minimize the detection delay subject to constraints on false alarm probability. The false data injection can be modeled with a sparse attack vector, with each non-zero element corresponding to one meter under attack. Since neither the support nor the values of the sparse attack vector is known, a new orthogonal matching pursuit cumulative sum (OMP-CUSUM) algorithm is proposed to identify the meters under attack while minimizing the detection delay. In order to recover the support of the sparse vector, we develop a new stopping condition for the iterative OMP algorithm by analyzing the statistical properties of the power grid measurements. Theoretical analysis and simulation results show that the proposed OMP-CUSUM algorithm can efficiently identify the meters under attack, and reliably detect false data injections with low delays while maintaining good detection accuracy.},
doi = {10.1109/ACCESS.2018.2873981},
journal = {IEEE Access},
number = ,
volume = 6,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}
https://doi.org/10.1109/ACCESS.2018.2873981
Web of Science