Abstract
The Internet of Things (IoT) encompasses a vast network of interconnected devices and systems, usually with limited computational power, memory, and energy resources. These characteristics make IoT ecosystems prone to different cybersecurity problems that must be addressed. Model-based intrusion detection systems (IDS) are one the tools that are being researched and developed to help in the detection of anomalies in IoT networks.
This study investigates the behavior and impact of the latent space within an autoencoder, with a focus on developing an optimal decision tree-based classification model integrated into an IoT-specific Intrusion Detection System (IDS). To ensure the effectiveness of the autoencoder, various latent spaces are explored and evaluated. By utilizing a validated IoT dataset that specifically targets the CoAP (Constrained Application Protocol) protocol, we extract an optimized model tailored to detect attacks on this protocol. The results of our comprehensive evaluation demonstrate promising outcomes, validating the effectiveness of the proposed techniques.
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Acknowledgements
– Spanish National Cybersecurity Institute (INCIBE) and developed Research Institute of Applied Sciences in Cybersecurity (RIASC).
– CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Professional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).
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García-Ordás, M.T., Aveleira-Mata, J., García-Rodiguez, I., Díaz-Longueira, A.J., Calvo-Rolle, JL., Alaiz-Moretón, H. (2023). Impact of Autoencoder Latent Space on IoT CoAP Attack Categorization. In: Jove, E., Zayas-Gato, F., Michelena, Á., Calvo-Rolle, J.L. (eds) Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-031-38616-9_4
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