Autoencoder Latent Space Influence on IoT MQTT Attack Classification

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

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Book:
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 Fernández (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

DIALNET GOOGLE SCHOLAR lock_openOpen access editor

Abstract

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.

Data source: Dialnet