Anomaly Detection in Smart Grids: A Survey From Cybersecurity Perspective
Author:
Affiliation:
1. Hamad Bin Khalifa University,College of Science and Engineering,Doha,Qatar
Funder
Qatar National Research Fund
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9774031/9773331/09774221.pdf?arnumber=9774221
Reference60 articles.
1. Machine Leaning Algorithms and Time Series Feature Extraction Library for Electricity Consumption Fraud Detection in Smart Grids
2. Hierarchical Temporal Memory Based Machine Learning for Real-Time, Unsupervised Anomaly Detection in Smart Grid: WiP Abstract
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