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

Nan Ding1, Huanbo Gao2, Hongyu Bu3

  • 1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China. dingnan@dlut.edu.cn.

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|October 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces RADM, a novel real-time anomaly detection algorithm for multivariate time-series data. RADM combines Hierarchical Temporal Memory and Bayesian Networks to significantly improve anomaly detection performance.

Keywords:
anomaly detectionbayesian networkhierarchical temporal memorymultivariate-sensing time-series

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Anomaly detection is crucial for identifying system irregularities from sensor data.
  • Detecting anomalies in multivariate time-series data presents significant challenges.
  • Real-time monitoring of system states in cloud platforms requires efficient anomaly detection.

Purpose of the Study:

  • To propose RADM, a real-time anomaly detection algorithm for multivariate-sensing time-series.
  • To leverage Hierarchical Temporal Memory (HTM) and Bayesian Networks (BN) for enhanced anomaly detection.
  • To demonstrate the effectiveness of RADM in real-time system state monitoring.

Main Methods:

  • Utilizing HTM to evaluate real-time anomalies in individual univariate time-series.
  • Employing a Naive Bayesian model for anomalous state detection in multivariate time-series.
  • Validating the algorithm's performance using simulated data for cloud platform terminal node monitoring.

Main Results:

  • RADM effectively detects anomalies in multivariate-sensing time-series.
  • The proposed algorithm shows improved performance in real-time anomaly detection.
  • Simulation results confirm the methodology's effectiveness.

Conclusions:

  • RADM offers a robust solution for real-time anomaly detection in complex systems.
  • The integration of HTM and BN enhances the accuracy and efficiency of anomaly identification.
  • This approach is valuable for real-time monitoring applications, particularly in cloud environments.