@Article{cmc.2025.067818,
AUTHOR = {Remah Younisse, Mouhammd AlKasassbeh, Amjad Aldweesh},
TITLE = {A Fog-Based Approach for Theft Detection and Zero-Day Attack Prevention in Smart Grid Systems},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {85},
YEAR = {2025},
NUMBER = {3},
PAGES = {4921--4941},
URL = {http://www.techscience.com/cmc/v85n3/64161},
ISSN = {1546-2226},
ABSTRACT = {Smart grid systems are advancing electrical services, making them more compatible with Internet of Things (IoT) technologies. The deployment of smart grids is facing many difficulties, requiring immediate solutions to enhance their practicality. Data privacy and security are widely discussed, and many solutions are proposed in this area. Energy theft attacks by greedy customers are another difficulty demanding immediate solutions to decrease the economic losses caused by these attacks. The tremendous amount of data generated in smart grid systems is also considered a struggle in these systems, which is commonly solved via fog computing. This work proposes an energy-theft detection method for smart grid systems employed in a fog-based network infrastructure. This work also proposes and analyzes Zero-day energy theft attack detection through a multi-layered approach. The detection process occurs at fog nodes via five machine-learning classification models. The performance of the classifiers is measured, validated, and reported for all models at fog nodes, as well as the required training and testing time. Finally, the measured results are compared to when the detection process occurs at a central processing unit (cloud server) to investigate and compare the performance metrics’ goodness. The results show comparable accuracy, precision, recall, and F1-measure performance. Meanwhile, the measured execution time has decreased significantly in the case of the fog-based network infrastructure. The fog-based model achieved an accuracy and recall of 98%, F1 score of 99%, and reduced detection time up to around 85% compared to the cloud-based approach.},
DOI = {10.32604/cmc.2025.067818}
}