Deep Anomaly Detection Framework Utilizing Federated Learning for Electricity Theft Zero-Day Cyberattacks
A Alshehri, MM Badr, M Baza, H Alshahrani - Sensors, 2024 - mdpi.com
Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers
compromise their smart meters (SMs) to downscale the reported electricity consumption …
compromise their smart meters (SMs) to downscale the reported electricity consumption …
Robust detection of electricity theft against evasion attacks in smart grids
Electricity theft cyber-attacks pose significant threats to smart power grids. In these attacks,
malicious customers hack into their smart meters and manipulate the integrity of their energy …
malicious customers hack into their smart meters and manipulate the integrity of their energy …
Deep autoencoder-based anomaly detection of electricity theft cyberattacks in smart grids
Designing an electricity theft cyberattack detector for the advanced metering infrastructures
(AMIs) is challenging due to the limited availability of electricity theft datasets (ie, malicious …
(AMIs) is challenging due to the limited availability of electricity theft datasets (ie, malicious …
Deep learning detection of electricity theft cyber-attacks in renewable distributed generation
Unlike the existing research that focuses on detecting electricity theft cyber-attacks in the
consumption domain, this paper investigates electricity thefts at the distributed generation …
consumption domain, this paper investigates electricity thefts at the distributed generation …
Detection of electricity theft false data injection attacks in smart grids
Malicious customers hack into their smart meters to reduce their electricity bills using various
cyberattack types. Such actions lead to financial losses and stability issues in the power grid …
cyberattack types. Such actions lead to financial losses and stability issues in the power grid …
Review of the data-driven methods for electricity fraud detection in smart metering systems
In smart grids, homes are equipped with smart meters (SMs) to monitor electricity
consumption and report fine-grained readings to electric utility companies for billing and …
consumption and report fine-grained readings to electric utility companies for billing and …
Electricity theft detection using deep reinforcement learning in smart power grids
AT El-Toukhy, MM Badr, MMEA Mahmoud… - IEEE …, 2023 - ieeexplore.ieee.org
In smart power grids, smart meters (SMs) are deployed at the end side of customers to report
fine-grained power consumption readings periodically to the utility for energy management …
fine-grained power consumption readings periodically to the utility for energy management …
Clustering and Ensemble Based Approach For Securing Electricity Theft Detectors Against Evasion Attacks
In smart power grids, electricity theft causes huge economic losses to electrical utility
companies. Machine learning (ML), especially deep neural network (DNN) models hold …
companies. Machine learning (ML), especially deep neural network (DNN) models hold …
Electricity Theft Detection Approach Using One-Class Classification for AMI
The utilization of Advanced Metering Infrastructure (AMI) technology is for recording and
billing customers for electricity consumption. This technology is vulnerable to cyber-attacks …
billing customers for electricity consumption. This technology is vulnerable to cyber-attacks …
Fine-tuned rnn-based detector for electricity theft attacks in smart grid generation domain
In this article, we investigate the problem of electricity theft attacks on smart meters when
malicious customers (ie, adversaries) claim injecting more generated energy into the grid to …
malicious customers (ie, adversaries) claim injecting more generated energy into the grid to …