Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series  

作　　者：Byeongcheon Lee Sangmin Kim Muazzam Maqsood Jihoon Moon Seungmin Rho 

机构地区：[1]Department of Security Convergence,Chung-Ang University,Seoul,06974,Republic of Korea [2]Department of Computer Science,COMSATS University Islamabad,Attock Campus,Attock,43600,Pakistan [3]Department of AI and Big Data,Soonchunhyang University,Asan,31538,Republic of Korea [4]Department of Industrial Security,Chung-Ang University,Seoul,06974,Republic of Korea

出　　处：《Computers, Materials & Continua》2024年第10期1275-1300,共26页计算机、材料和连续体（英文）

基　　金：supported by the Culture,Sports,and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports,and Tourism in 2024(Project Name:Development of Distribution and Management Platform Technology and Human Resource Development for Blockchain-Based SW Copyright Protection,Project Number:RS-2023-00228867,Contribution Rate:100%)and also supported by the Soonchunhyang University Research Fund.

摘　　要：In the context of rapid digitization in industrial environments,how effective are advanced unsupervised learning models,particularly hybrid autoencoder models,at detecting anomalies in industrial control system(ICS)datasets?This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things(IoT)devices,which can significantly improve the reliability and safety of these systems.In this paper,we propose a hybrid autoencoder model,called ConvBiLSTMAE,which combines convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)to more effectively train complex temporal data patterns in anomaly detection.On the hardware-in-the-loopbased extended industrial control system dataset,the ConvBiLSTM-AE model demonstrated remarkable anomaly detection performance,achieving F1 scores of 0.78 and 0.41 for the first and second datasets,respectively.The results suggest that hybrid autoencoder models are not only viable,but potentially superior alternatives for unsupervised anomaly detection in complex industrial systems,offering a promising approach to improving their reliability and safety.

关 键 词：Advanced anomaly detection autoencoder innovations unsupervised learning industrial security multivariate time series analysis 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]