TY - JOUR
T1 - Anomaly Detection Using Multiscale C-LSTM for Univariate Time-Series, CCF C
AU - Lu, Yi Xiang
AU - Jin, Xiao Bo
AU - Liu, Dong Jie
AU - Zhang, Xin Chang
AU - Geng, Guang Gang
N1 - Publisher Copyright:
© 2023 Yi-Xiang Lu et al.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85147547021&partnerID=8YFLogxK
U2 - 10.1155/2023/6597623
DO - 10.1155/2023/6597623
M3 - Article
AN - SCOPUS:85147547021
SN - 1939-0114
VL - 2023
JO - Security and Communication Networks
JF - Security and Communication Networks
M1 - 6597623
ER -