Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of...

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Detalles Bibliográficos
Autores principales: Filus, Katarzyna, Domański, Adam, Domańska, Joanna, Marek, Dariusz, Szyguła, Jakub
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597326/
https://www.ncbi.nlm.nih.gov/pubmed/33286928
http://dx.doi.org/10.3390/e22101159

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597326/
https://www.ncbi.nlm.nih.gov/pubmed/33286928
http://dx.doi.org/10.3390/e22101159

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