Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly d...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 18; no. 10; p. 3367
Main Authors: Ding, Nan, Gao, Huanbo, Bu, Hongyu, Ma, Haoxuan, Si, Huaiwei
Format: Journal Article
Language:English
Published: Switzerland MDPI 09.10.2018
MDPI AG
Subjects:
anomaly detection
bayesian network
hierarchical temporal memory
multivariate-sensing time-series
bayesian network
anomaly detection
multivariate-sensing time-series
hierarchical temporal memory
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s18103367