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.
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Acknowledgements
This work is partially supported by:
– Spanish National Cybersecurity Institute (INCIBE) and developed Research Institute of Applied Sciences in Cybersecurity (RIASC).
– Junta de Castilla y León - Consejería de Educación. Project: LE078G18. UXXI2018/000149. U-220.
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García-Ordás, M.T., Aveleira-Mata, J., Casteleiro-Roca, JL., Calvo-Rolle, J.L., Benavides-Cuellar, C., Alaiz-Moretón, H. (2020). Autoencoder Latent Space Influence on IoT MQTT Attack Classification. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_27
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