Abstract
This research paper is focused on security in IoT devices network, by providing a visual tool based on Beta Hebbian Learning (BHL) to easily identify attacks in the network to human experts. Contrary to Artificial Intelligent-driven solutions based on supervised learning BHL does not require labelled information. A testing environment of IoT devices and web clients is created and attack by using a Sybil Attack type, recording all traffic information in separating the fields of the captures by the most relevant, these fields are taken from “Wireshark Display Filter Reference” tool. Results obtained by BHL algorithm provide clear projections where most of the attacks can be easily identified by human expert through visual inspection in real time, proving a powerful tool to be easily implemented in IoT environments.
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Acknowledgements
CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).
Spanish National Cybersecurity Institute (INCIBE) and developed Research Institute of Applied Sciences in Cybersecurity (RIASC).
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Grandío, Á.M. et al. (2022). Beta Hebbian Learning for Intrusion Detection in Networks of IoT Devices. In: Gude Prego, J.J., de la Puerta, J.G., García Bringas, P., Quintián, H., Corchado, E. (eds) 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021). CISIS - ICEUTE 2021. Advances in Intelligent Systems and Computing, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-87872-6_3
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