Machine Learning-based Cybersecurity Defence of Wide-area Monitoring Systems
Author:
Affiliation:
1. University of Queensland,Schoold of Information Technology and Electrical Engineering
2. Energy Queensland,Department of Renewables & Distributed Energy,Brisbane,Australia
Funder
Advance Queensland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9948701/9949620/09949686.pdf?arnumber=9949686
Reference21 articles.
1. A Prediction Algorithm to Enhance Grid Resilience Toward Cyber Attacks in WAMCS Applications
2. Source Authentication of Distribution Synchrophasors for Cybersecurity of Microgrids
3. Spatio-Temporal Synchrophasor Data Characterization for Mitigating False Data Injection in Smart Grids
4. Source Location Identification of Distribution-Level Electric Network Frequency Signals at Multiple Geographic Scales
5. Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge;2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);2024-02-19
2. Deep Learning Application in Continuous Authentication;Lecture Notes in Electrical Engineering;2024
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