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

Autor:	Katarzyna Filus, Adam Domański, Joanna Domańska, Dariusz Marek, Jakub Szyguła
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	neural networks convolutional neural networks Hurst exponent self-similarity Internet traffic fractional Gaussian noise Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 22, Iss 10, p 1159 (2020)
Druh dokumentu:	article
ISSN:	1099-4300
DOI:	10.3390/e22101159
Popis:	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 classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/621d4838697f4321a56dc22ebca4540e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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