Abstract
This publication focuses on the use of a fuzzy neural network for data classification in the context of IPv6 routing attack detection. The research methodology includes a comparison of the proposed scalable fuzzy neural network, utilizing Ordered Fuzzy Numbers, with well-known solutions, such as Artificial Neural Networks. A portion of the ROUT-4-2023 dataset was used in the experiment. The results demonstrate that this implementation could be effectively utilized for data classification in small IoT solutions. The conclusions provide a discussion on the limitations, future research prospects, and recommendations for further work.
Paper Type
Poster
DOI
10.62036/ISD.2025.54
Fuzzy scalable neural network for IPv6 network security
This publication focuses on the use of a fuzzy neural network for data classification in the context of IPv6 routing attack detection. The research methodology includes a comparison of the proposed scalable fuzzy neural network, utilizing Ordered Fuzzy Numbers, with well-known solutions, such as Artificial Neural Networks. A portion of the ROUT-4-2023 dataset was used in the experiment. The results demonstrate that this implementation could be effectively utilized for data classification in small IoT solutions. The conclusions provide a discussion on the limitations, future research prospects, and recommendations for further work.
Recommended Citation
Apiecionek, L., Cybulski, J. & Krzysztoń, E. (2025). Fuzzy scalable neural network for IPv6 network securityIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.54