Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series
Yi-Xiang Lu, Xiaobo Jin, Liu Dong-jie, Xinchang Zhang, Guang-Gang Geng
Abstract
Computers generate network traffic data when people go online, and devices generate sensor data when they communicate with each other. When events such as network intrusion or equipment failure occur, the corresponding time-series will show abnormal trends. By detecting these time-series, anomalous events can be detected instantly, ensuring the security of network communication. However, existing time-series anomaly detection methods are difficult to deal with sequences with different degrees of correlation in complex scenes. In this paper, we propose three multiscale C-LSTM deep learning models to efficiently detect abnormal time-series: independent multiscale C-LSTM (IMC-LSTM), where each LSTM has an independent scale CNN; combined multiscale C-LSTM (CMC-LSTM), that is, the output of multiple scales of CNN is combined as an LSTM input; and shared multiscale C-LSTM (SMC-LSTM), that is, the output of multiple scales of CNN shares an LSTM model. Comparative experiments on multiple data sets show that the proposed three models have achieved excellent performance on the famous Yahoo Webscope S5 dataset and Numenta Anomaly Benchmark dataset, even better than the existing C-LSTM based latest model.