Autoencoder Latent Space Influence on IoT MQTT Attack Classification
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María Teresa García-Ordás [1] [1] ; Jose Aveleira-Mata [1] ; José-Luis Casteleiro-Roca [2] ; José Luis Calvo-Rolle [2] ; Carmen Benavides-Cuellar [1] [2] ; Héctor Alaiz-Moretón [1] [1]
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[1] Universidad de León
Universidad de León
León, España
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[2] Universidade da Coruña
Universidade da Coruña
A Coruña, España
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- Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.), David Camacho Fernández (ed. lit.), Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 279-286
- Idioma: inglés
- Enlaces
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Resumen
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.
