Semi-Markov switching vector autoregressive model-based anomaly detection in aviation systems

Igor Melnyk, Arindam Banerjee, Bryan Matthews, Nikunj Oza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

In this work we consider the problem of anomaly detection in heterogeneous, multivariate, variable-length time series datasets. Our focus is on the aviation safety domain, where data objects are flights and time series are sensor readings and pilot switches. In this context the goal is to detect anomalous flight segments, due to mechanical, environmental, or human factors in order to identifying operationally significant events and highlight potential safety risks. For this purpose, we propose a framework which represents each fight using a semi-Markov switching vector autoregressive (SMS-VAR) model. Detection of anomalies is then based on measuring dissimilarities between the model's prediction and data observation. The framework is scalable, due to the inherent parallel nature of most computations, and can be used to perform online anomaly detection. Extensive experimental results on simulated and real datasets illustrate that the framework can detect various types of anomalies along with the key parameters involved.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1065-1074
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Other

Other22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period8/13/168/17/16

Bibliographical note

Funding Information:
This work was supported by NASA grant NNX12AQ39A, NSF Grants IIS-0953274, IIS-1029711, IIS-0916750. We acknowledge the computing support from Minnesota Supercomputing Institute (MSI). A. B. acknowledges support from Adobe, IBM, and Yahoo

Publisher Copyright:
© 2016 ACM.

Keywords

  • Anomaly detection
  • Graphical model
  • Time series analysis

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