Real-time anomaly detection based on long short-Term memory and Gaussian Mixture Model. (October 2019)

• Record Type: Journal Article
• Authors: Ding, Nan; Ma, HaoXuan; Gao, Huanbo; Ma, YanHua; Tan, GuoZhen
• Is Part Of: Computers & electrical engineering. Volume 79(2019)
• Publication Date: 2019-10
• Subjects: Anomaly detection -- Long short term memory -- Gaussian mixture model -- Multivariate sensing time series; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.106458
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Anomaly detection is a long-standing problem in system designation. High-quality anomaly detection can benefit plenty of applications (e.g. system monitoring, disaster precaution and intrusion detection). Most of the existing anomalies detection algorithms are less competent for both effectiveness and real-time capability requirements simultaneously. Therefore, in this paper, the LGMAD, a real-time anomaly detection algorithm based on Long-Short Term Memory (LSTM) and Gaussian Mixture Model (GMM)is proposed. Specifically, we evaluate the real-time anomalies of each univariate sensing time-series via LSTM model, and then a Gaussian Mixture Model is adopted to give a multidimensional joint detection of possible anomalies. Both NAB dataset and self-made dataset are employed to verify our approach. Extensive experiments are conducted to demonstrate the superiority of LGMAD compared to existing anomaly detection algorithms.