Abstract
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.
Highlights
The system condition monitoring associated with virus invasion, failed sensors, and improperly implemented controls plagues many automated information system, such as wireless sensor networking, vehicular networking, and industrial system
The real-time anomaly detection for system condition monitoring has significant and practical applications, which uses the information coming in real-time from different sensors and other condition information sources and tries to detect possible anomalies in the normal condition and behaviour expected of its devices or components
The linear dimensionality reduction method used for multivariate time series (MTS) based on common principle component analysis, are processing the principle time series according to the anomaly detection method for univariate time series (UTS) [14]
Summary
The system condition monitoring associated with virus invasion, failed sensors, and improperly implemented controls plagues many automated information system, such as wireless sensor networking, vehicular networking, and industrial system. In a complex system, compared to the anomaly detection in UTS, it brings richer system information using multivariate time series (MTS), which are captured from the sensors and condition information sources. Existing dimensionality reduction methods, such as PCA (Principal component analysis) dimensionality reduction method used for MTS and the linear dimensionality reduction method used for MTS based on common principle component analysis, are processing the principle time series according to the anomaly detection method for UTS [14] Another method is to take the sliding window as a tool for dividing MTS, and detect the subsequences. RADM combines HTM with naive Bayesian network to detect anomalies in multivariate-sensing time-series, and get better result compared with the algorithm just work in univariate-sensing time-series
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
