Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

作者：Chunyong Yin, Sun Zhang, Jin Wang, Naixue Xiong · 发表于：IEEE Transactions on Systems Man and Cybernetics Systems · 年份：2020 · DOI：10.1109/tsmc.2020.2968516 · 被引用次数：412 · 研究领域：Anomaly Detection Techniques and Applications、Time Series Analysis and Forecasting、Network Security and Intrusion Detection

Internet of Things (IoT) realizes the interconnection of heterogeneous devices by the technology of wireless and mobile communication. The data of target regions are collected by widely distributed sensing devices and transmitted to the processing center for aggregation and analysis as the basis of IoT. The quality of IoT services usually depends on the accuracy and integrity of data. However, due to the adverse environment or device defects, the collected data will be anomalous. Therefore, the effective method of anomaly detection is the crucial issue for guaranteeing service quality. Deep learning is one of the most concerned technology in recent years which realizes automatic feature extraction from raw data. In this article, the integrated model of the convolutional neural network (CNN) and recurrent autoencoder is proposed for anomaly detection. Simple combination of CNN and autoencoder cannot improve classification performance, especially, for time series. Therefore, we utilize the two-stage sliding window in data preprocessing to learn better representations. Based on the characteristics of the Yahoo Webscope S5 dataset, raw time series with anomalous points are extended to fixed-length sequences with normal or anomaly label via the first-stage sliding window. Then, each sequence is transformed into continuous time-dependent subsequences by another smaller sliding window. The preprocessing of the two-stage sliding window can be considered as low-level temporal feature ...