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The Artificial Immune Ecosystem: A Bio-Inspired Meta-Algorithm for Boosting Time Series Anomaly Detection with Expert Input

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Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

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Abstract

One of the challenges in machine learning, especially in the Big Data era, is to obtain labeled data sets. Indeed, the difficulty of labeling large amounts of data had lead to an increasing reliance on unsupervised classifiers, such as deep autoencoders. In this paper, we study the problem of involving a human expert in the training of a classifier instead of using labeled data. We use anomaly detection in network monitoring as a field of application. We demonstrate how using crude, already existing monitoring software as a heuristic to choose which points to label can boost the classification rate with respect to both the monitoring software and the classifier trained on a fully labeled data set, with a very low computational cost. We introduce the Artificial Immune Ecosystem meta-algorithm as a generic framework integrating the expert, the heuristic and the classifier.

The work presented here has been funded by IPLine SAS, by the French ANRT in the frame of CIFRE contract 2015/0079, and by the French Banque Publique d’Investissement (BPI) under program FUI-AAP-19 in the frame of the HuMa project.

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Notes

  1. 1.

    Personal experience of the author as a software engineer for a Cloud service provider.

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Author information

Authors and Affiliations

  1. IPLine, Caluire-et-cuire, France

    Fabio Guigou

  2. ICube Laboratory, Université de Strasbourg, Strasbourg, France

    Fabio Guigou, Pierre Collet & Pierre Parrend

  3. ECAM Strasbourg-Europe, Schiltigheim, France

    Pierre Parrend

  4. Complex System Digital Campus (UNESCO Unitwin), Paris, France

    Fabio Guigou, Pierre Collet & Pierre Parrend

Authors
  1. Fabio Guigou
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  2. Pierre Collet
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  3. Pierre Parrend
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Corresponding author

Correspondence to Fabio Guigou .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Guigou, F., Collet, P., Parrend, P. (2017). The Artificial Immune Ecosystem: A Bio-Inspired Meta-Algorithm for Boosting Time Series Anomaly Detection with Expert Input. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

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