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Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2020, IoT Streams 2020)

Abstract

Communication networks are complex systems consisting of many components each producing a multitude of system metrics that can be monitored in real-time. Anomaly Detection (AD) allows to detect deviant behavior in these system metrics. However, in communication networks, large amounts of domain knowledge and huge manual efforts are required to efficiently monitor these complex systems. In this paper, we describe how AutoEncoders (AE) can elevate the manual effort for unsupervised AD in communication networks. We show that AE can be applied, without domain knowledge or manual effort and evaluate different types of AE architectures and how they perform on a variety of anomaly types found in communication networks.

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Notes

  1. 1.

    https://skyline.be/.

  2. 2.

    https://github.com/KDD-OpenSource/agots.

  3. 3.

    We note that the technique does require some hyperparameter tuning.

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Acknowledgement

This research was funded by the imec.icon project RADIANCE, which was co-financed by imec, VLAIO, Barco, ML6 and Skyline.

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Correspondence to Pieter Bonte .

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Bonte, P. et al. (2020). Unsupervised Anomaly Detection for Communication Networks: An Autoencoder Approach. In: Gama, J., et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-66770-2_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66769-6

  • Online ISBN: 978-3-030-66770-2

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