Autoencoder Latent Space Influence on IoT MQTT Attack Classification

  1. María Teresa García-Ordás 11
  2. Jose Aveleira-Mata 1
  3. José-Luis Casteleiro-Roca 2
  4. José Luis Calvo-Rolle 2
  5. Carmen Benavides-Cuellar 12
  6. Héctor Alaiz-Moretón 11
  1. 1 Universidad de León

    Universidad de León

    León, España


  2. 2 Universidade da Coruña

    Universidade da Coruña

    La Coruña, España


Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings
  1. Cesar Analide (ed. lit.)
  2. Paulo Novais (ed. lit.)
  3. David Camacho (ed. lit.)
  4. Hujun Yin (ed. lit.)

Publisher: Springer International Publishing AG

ISBN: 978-3-030-62362-3 978-3-030-62361-6 978-3-030-62364-7 978-3-030-62365-4

Year of publication: 2020

Volume Title: Part II

Volume: 2

Pages: 279-286

Congress: Intelligent Data Engineering and Automated Learning – IDEAL (21. 2020. Guimarães)

Type: Conference paper


IoT (Internet of Things) alludes to many different devices and systems connected to Internet, being 5 billion the number of these devices working around the world actually. The security policies applied to this kind of systems can be improve due to their behaviour, usually associated to their low price and low computing capacity.This work addresses the behaviour and impact of latent space of an auto-encoder for creating a classification model based on decision trees, in order to include it in a IDS (Intrusion Detection System) specialized in IoT environments. A validate IoT dataset, based on MQTT (Message Queue Telemetry Transport), has been used for applied the techniques implemented for extracting an optimal model oriented to detect the attacks over this protocol with a suitable results.