AUTHOR=Kai Li , Pingyan Mo , Yongjiao Yang , Hanyang Xie , Zhixiong Shen 

TITLE=Abnormality detection and privacy protection strategies for power marketing inspection business of cyber–physical–social systems using big data and artificial intelligence

JOURNAL=Frontiers in Physics

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1594819

DOI=10.3389/fphy.2025.1594819

ISSN=2296-424X

ABSTRACT=The widespread adoption of cyber–physical–social systems (CPSSs) in the power industry has necessitated power marketing inspection as a critical component for ensuring secure and reliable operations of power systems. However, this effort entails significant challenges resulting from the massive volume of inspection data and complexity of electricity usage scenarios. Traditional inspection methods often fail to identify potential risks and abnormal behaviors effectively; to address this, we propose an intelligent security identification model for CPSS-based power marketing inspection by integrating advanced artificial intelligence techniques to enhance security defense and risk management. The proposed model incorporates a work order correlation matching algorithm, a fault interval detection algorithm, an electricity consumption prediction algorithm, and a business anomaly identification algorithm. Here, the users are first categorized based on multisource data to detect abnormal electricity usage precisely. Then, the model employs a correlation algorithm to uncover the intrinsic links between fault handling and electricity refund work orders for the same user, thereby revealing potential security vulnerabilities. Subsequently, the fault interval detection algorithm is used to locate fault periods, and the electricity consumed within these intervals is dynamically estimated using a prediction algorithm. Finally, an intelligent classification model based on recurrent neural networks and long short-term memory networks is developed by leveraging key security features to identify abnormal business behaviors accurately. Experiments were then conducted on three publicly available power industry datasets, and the results demonstrate that the proposed model significantly outperforms traditional methods in terms of accuracy, recall, and F1-score for security event detection. The proposed approach effectively enhances the safety and reliability of power marketing inspection for CPSSs while offering a novel technical framework for power system protection and privacy preservation.