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Distributed Anomalies Detection Using Isolation Forest and Spark

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Advances in Computational Collective Intelligence (ICCCI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1653))

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Abstract

Anomaly detection is a major issue for several applications such as industrial failure detection, cybersecurity or transport. Several approaches, such as statistical methods, machine learning and sketch, have been explored by different research communities to detect anomalies in an increasingly challenging context. Indeed, facing the huge volume of data generated at an increasingly fast speed, the response time of the algorithms and their distributivity have become determining criteria, in addition to their accuracy in detecting anomalies. We focus in this paper on the unsupervised anomaly detection algorithm based on binary trees: Isolation Forest. It is a very powerful algorithm with an excellent accuracy and a very low execution time thanks to its linear complexity. In particular, we study the architecture of two distribution solutions of Isolation Forest based on the Apache Spark framework. We then compare the performance of these two solutions by testing them against 4 real commonly used datasets.

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Notes

  1. 1.

    https://spark.apache.org/.

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Author information

Authors and Affiliations

  1. ISEP - Institut Supérieur d’Électronique de Paris, 10 rue de Vanves, 92130, Issy les Moulineaux, France

    Maurras Ulbricht Togbe & Yousra Chabchoub

  2. Faculté des Sciences et Techniques (FST)/Département Mathématiques et Informatique, Université Cheikh Anta Diop de Dakar, BP5005, Dakar-Fann, Senegal

    Aliou Boly

  3. 3DS OUTSCALE, 1 Rue Royale, Saint Cloud, France

    Raja Chiky

Authors
  1. Maurras Ulbricht Togbe
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  2. Yousra Chabchoub
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  3. Aliou Boly
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  4. Raja Chiky
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Corresponding author

Correspondence to Maurras Ulbricht Togbe .

Editor information

Editors and Affiliations

  1. University of Craiova, Craiova, Romania

    Costin Bădică

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. Claude Bernard University Lyon 1, Villeurbanne Cedex, France

    Djamal Benslimane

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Marek Krótkiewicz

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Togbe, M.U., Chabchoub, Y., Boly, A., Chiky, R. (2022). Distributed Anomalies Detection Using Isolation Forest and Spark. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_57

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  • DOI: https://doi.org/10.1007/978-3-031-16210-7_57

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  • Online ISBN: 978-3-031-16210-7

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