Multi-View Convolutional Neural Network for Data Spoofing Cyber-Attack Detection in Distribution Synchrophasors

Journal Article · Mon Feb 03 00:00:00 EST 2020 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/tsg.2020.2971148· OSTI ID:1648880

Qiu, Wei ^[1]; Tang, Qiu ^[1]; Wang, Yajun ^[2]; Zhan, Lingwei ^[3]; Liu, Yilu ^[2]; Yao, Wenxuan ^[2]

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1. Hunan Univ., Changsha (China)
2. Univ. of Tennessee, Knoxville, TN (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. In order to address this issue, a source authentication based data spoofing attack detection method is proposed using Multi-view Convolutional Neural Network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1648880
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 4 Vol. 11; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
data spoofing attack
distribution synchrophasors data
multi-view convolutional neural network
source authentication