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


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Nayat Sanchez-Pi , Luis Martí , Álvaro Luis Bustamante , José M. Molina

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. More... »

PAGES

259-269

References to SciGraph publications

  • 2016. Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XIV
  • 2012-12. A review of wearable sensors and systems with application in rehabilitation in JOURNAL OF NEUROENGINEERING AND REHABILITATION
  • 2002. A Geometric Framework for Unsupervised Anomaly Detection in APPLICATIONS OF DATA MINING IN COMPUTER SECURITY
  • 2004. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors in GENETIC AND EVOLUTIONARY COMPUTATION – GECCO 2004
  • Book

    TITLE

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

    ISBN

    978-3-319-94778-5
    978-3-319-94779-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-94779-2_23

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    http://dx.doi.org/10.1007/978-3-319-94779-2_23

    DIMENSIONS

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