A Novel Convolutional Long Short-Term Memory Approach for Anomaly Detection in Power Monitoring System

Hao Zhang, Jing Wang, Xuanyuan Wang, Xinyi Feng, Hongda Gao and Yingchun Niu ()
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Hao Zhang: State Grid Jibei Electric Power Company Limited, Beijing 100054, China
Jing Wang: Beijing Kedong Electric Power Control System Co., Ltd., Beijing 100192, China
Xuanyuan Wang: State Grid Jibei Electric Power Company Limited, Beijing 100054, China
Xinyi Feng: Beijing Kedong Electric Power Control System Co., Ltd., Beijing 100192, China
Hongda Gao: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
Yingchun Niu: School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China

Energies, 2025, vol. 18, issue 18, 1-14

Abstract: With the rapid advancement of artificial intelligence, machine learning and big data analytics have become essential tools for enhancing the cybersecurity of power monitoring systems. This study proposes a network traffic anomaly detection model based on Convolutional Long Short-Term Memory (C-LSTM) networks, which integrates convolutional layers to capture spatial features and LSTM layers to model long-term temporal dependencies in network traffic. Incorporated into a cybersecurity situation awareness platform, the model enables comprehensive data collection, intelligent analysis, and rapid response to cybersecurity incidents, significantly enhancing the system’s ability to detect, warn, and mitigate potential threats. Experimental evaluations on the CICIDS2017 dataset demonstrate that the proposed model achieves high accuracy (95.3%) and recall (94.7%), highlighting its effectiveness and potential for practical application in safeguarding critical infrastructure against evolving cybersecurity challenges.

Keywords: Long Short-Term Memory (LSTM); traffic anomaly detection; power monitoring system; cybersecurity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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