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Time Series Anomaly Detection with Variational Autoencoder Using Mahalanobis Distance

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ICT Innovations 2020. Machine Learning and Applications (ICT Innovations 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1316))

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Abstract

Two themes have dominated the research on anomaly detection in time series data, one related to explorations of deep architectures for the task, and the other, equally important, the creation of large benchmark datasets. In line with the current trends, we have proposed several deep learning architectures based on Variational Autoencoders that have been evaluated for detecting cyber-attacks on water distribution system on the BATADAL challenge task and dataset. The second research aim of this study was to examine the impact of using Mahalanobis distance as a reconstruction error on the performance of the proposed models.

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Notes

  1. 1.

    https://www.batadal.net/data.html.

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Acknowledgement

This work was partially financed by the Faculty of Computer Science and Engineering at the “Ss. Cyril and Methodius” University.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, University of Sts. Cyril and Methodius in Skopje, Rugjer Boshkovikj 16, Skopje, Republic of North Macedonia

    Laze Gjorgiev & Sonja Gievska

Authors
  1. Laze Gjorgiev
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  2. Sonja Gievska
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Correspondence to Laze Gjorgiev .

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Editors and Affiliations

  1. University Ss. Cyril and Methodius, Skopje, North Macedonia

    Vesna Dimitrova

  2. University Ss. Cyril and Methodius, Skopje, North Macedonia

    Ivica Dimitrovski

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Gjorgiev, L., Gievska, S. (2020). Time Series Anomaly Detection with Variational Autoencoder Using Mahalanobis Distance. In: Dimitrova, V., Dimitrovski, I. (eds) ICT Innovations 2020. Machine Learning and Applications. ICT Innovations 2020. Communications in Computer and Information Science, vol 1316. Springer, Cham. https://doi.org/10.1007/978-3-030-62098-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-62098-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62097-4

  • Online ISBN: 978-3-030-62098-1

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