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How Machine Learning Could Detect Anomalies on Thinger.io Platform?

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Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection (PAAMS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 887))

Abstract

This research explores the capacity of Machine Learning techniques to detect anomalies and how incorporate this capacity to thinger.io platform. Thinger.io is a IoT opensource platform that allows to create an IoT environment using any hardware available on market. In this paper, several ML techniques are proposed to detect anomalies in the platform.

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Acknowledgements

This work was supported in part by Project MINECO TEC2017-88048-C2-2-R, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015, CNPq Universal 430082/2016-9, FAPERJ JCNE E-26/203.287/2017, Project Prociência 2017-038625-0, CNPq PQ 312792/2017-4.

Author information

Authors and Affiliations

  1. Institute of Mathematics and Statistics, Rio de Janeiro State University, Rio de Janeiro, Brazil

    Nayat Sanchez-Pi

  2. Institute of Computing, Fluminense Federal Univertity, Niterói, Brazil

    Luis Martí

  3. Computer Science Department, Carlos III University of Madrid, Madrid, Spain

    Álvaro Luis Bustamante & José M. Molina

Authors
  1. Nayat Sanchez-Pi
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  2. Luis Martí
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  3. Álvaro Luis Bustamante
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  4. José M. Molina
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Corresponding author

Correspondence to Nayat Sanchez-Pi .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Javier Bajo

  2. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Juan M. Corchado

  3. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena María Navarro Martínez

  4. TECNALIA, Derio, Spain

    Eneko Osaba Icedo

  5. Lille University of Science and Technology, Villeneuve d’Ascq, France

    Philippe Mathieu

  6. Poznan University of Technology, Poznan, Poland

    Patrycja Hoffa-Dąbrowska

  7. Polytechnic University of Valencia, Valencia, Spain

    Elena del Val

  8. Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada

    Sylvain Giroux

  9. University of Porto, Porto, Portugal

    Antonio J.M. Castro

  10. Rio de Janeiro State University, Rio de Janeiro, Brazil

    Nayat Sánchez-Pi

  11. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julián

  12. Informatica e Estatistica, Universidade Federal de Santa Catar, Florianopolis, Santa Catarina, Brazil

    Ricardo Azambuja Silveira

  13. Universidad Rey Juan Carlos, Móstoles, Madrid, Spain

    Alberto Fernández

  14. ICB, Universität Duisburg-Essen, Essen, Germany

    Rainer Unland

  15. Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

    Rubén Fuentes-Fernández

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Sanchez-Pi, N., Martí, L., Bustamante, Á.L., Molina, J.M. (2018). How Machine Learning Could Detect Anomalies on Thinger.io Platform?. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-94779-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94778-5

  • Online ISBN: 978-3-319-94779-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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