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A Complete Review onthe Application ofStatistical Methodsfor EvaluatingInternet TrafficUsage Vanice Canuto CunhaArturo Alejandro Zavala ZavalaDamien Magoni2022 год

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage
review

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Аннотация: Internet traffic classification aims to identify the kind of Internet traffic. With the rise of traffic encryption and multi-layer data encapsulation, some classic classification methods have lost their strength. In an attempt to increase classification performance, Machine Learning (ML) strategies have gained the scientific community interest and have shown themselves promising in the future of traffic classification, mainly in the recognition of encrypted traffic. However, some of these methods have a high computational resource consumption, which make them unfeasible for classification of large traffic flows or in real-time. Methods using statistical analysis have been used to classify real-time traffic or large traffic flows, where the main objective is to find statistical differences among flows or find a pattern in traffic characteristics through statistical properties that allow traffic classification. The purpose of this work is to address statistical methods to classify Internet traffic that were little or unexplored in the literature. This work is not generally focused on discussing statistical methodology. It focuses on discussing statistical tools applied to Internet traffic classification Thus, we provide an overview on statistical distances and divergences previously used or with potential to be used in the classification of Internet traffic. Then, we review previous works about Internet traffic classification using statistical methods, namely Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback–Leibler (KL) divergences, Support Vector Machines (SVM), Correlation Information (Pearson Correlation), Kolmogorov-Smirnov and Chi-Square tests, and Entropy. We also discuss some open issues and future research directions on Internet traffic classification using statistical methods.
Год издания: 2022
Авторы: Vanice Canuto Cunha, Arturo Alejandro Zavala Zavala, Damien Magoni, Pedro R. M. Inácio, Mário M. Freire
Издательство: Institute of Electrical and Electronics Engineers
Источник: IEEE Access
Ключевые слова: Internet Traffic Analysis and Secure E-voting, Network Security and Intrusion Detection, Advanced Malware Detection Techniques
Другие ссылки: IEEE Access (PDF)
IEEE Access (HTML)
HAL (Le Centre pour la Communication Scientifique Directe) (PDF)
HAL (Le Centre pour la Communication Scientifique Directe) (HTML)
DOAJ (DOAJ: Directory of Open Access Journals) (HTML)
HAL (Le Centre pour la Communication Scientifique Directe) (HTML)