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Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Detection of Electricity Theft in Customer Consumption Using Outlier Detection Algorithms
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Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters
inferredcites →https://doi.org/10.1109/icwapr.2015.7295946openalex-snapshot
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AdaBoost-SVM for Electrical Theft Detection and GRNN for Stealing Time Periods Identification
inferredcites →https://doi.org/10.1109/iwcmc48107.2020.9148314openalex-snapshot
CNN and GRU based Deep Neural Network for Electricity Theft Detection to Secure Smart Grid
inferredcites →https://doi.org/10.1109/jstsp.2018.2833749openalex-snapshot
Evaluating Detectors on Optimal Attack Vectors That Enable Electricity Theft and DER Fraud
inferredcites →https://doi.org/10.1109/jstsp.2019.2955012openalex-snapshot
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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
inferredcites →https://doi.org/10.1109/tii.2016.2543145openalex-snapshot
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid
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Non-Cooperative Game Model Applied to an Advanced Metering Infrastructure for Non-Technical Loss Screening in Micro-Distribution Systems
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Deep Learning Detection of Electricity Theft Cyber-Attacks in Renewable Distributed Generation
inferredcites →https://doi.org/10.1109/wcnc.2018.8377010openalex-snapshot
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Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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