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• W2022750209 abstract "Detailed knowledge about the traffic mixture is essential for network operators and administrators, as it is a key input for numerous network management activities. Several traffic classification approaches co-exist in the literature, but none of them performs well for all different application traffic types present in the Internet. In this study we compare and benchmark the currently known traffic classification methods on network traces captured in an operational 3G mobile network. Utilizing the experiences about the strengths and weaknesses of the existing approaches, a novel combined method is proposed aiming at improving the completeness and accuracy of classification. The novel method is based on a complex decision mechanism, which can provide appropriate identification for each different application type. As a main contribution, with the help of the new method it is shown that applications previously used only in fixed access networks may appear in mobile broadband environment." @default.
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• W2022750209 date "2007-10-22" @default.
• W2022750209 modified "2023-09-25" @default.
• W2022750209 title "Traffic analysis of mobile broadband networks" @default.
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• W2022750209 doi "https://doi.org/10.5555/1460047.1460070" @default.
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