基于M值概率分布的網(wǎng)絡視頻流分類

楊凌云; 董育寧; 王再見; 湯萍萍

doi:10.11999/JEIT170617

基于M值概率分布的網(wǎng)絡視頻流分類

doi: 10.11999/JEIT170617

2.
(南京郵電大學通信與信息工程學院南京 210003) ②(安徽師范大學物理與電子信息學院蕪湖 241000)

基金項目:

國家自然科學基金(61271233, 61401004, 61601005)，華為HIRP創(chuàng)新項目，安徽師范大學博士科研啟動金項目(2016XJJ129)

計量
- 文章訪問數(shù): 1165
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-06-28
- 修回日期: 2018-02-23
- 刊出日期: 2018-05-19

Network Video Traffic Classification Based on Probability Distribution of M Value

2.
(College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications,Nanjing 210003, China)

Funds:

The National Natural Science Foundation of China (61271233, 61401004, 61601005), The HIRP Program of Huawei Technology Co. Ltd, The Ph.D Programs Foundation of Anhui Normal University (2016XJJ129)

摘要

摘要: 為了改善網(wǎng)絡視頻流的細粒度分類效果，該文分析視頻流傳輸過程中的特征變化與流分類之間的關系。根據(jù)不同類型的視頻流具有不同的下行傳輸速率變化模式，提出一種新的基于下行速率傳輸?shù)囊曨l流分類特征--M值概率分布，并使用支持向量機(SVM)實現(xiàn)網(wǎng)絡視頻流的分類。實驗結果表明，M值概率分布相比較于已有的常見流特征，可以更好地實現(xiàn)6種典型的網(wǎng)絡視頻流分類。
- 網(wǎng)絡視頻流 /
- 流分類 /
- M值概率分布
Abstract: To obtain better results for fine-grained video traffic classification, this paper analyzes the relationship between the feature variations during transmission and video traffic classification. According to the nature that different types of video services contain different downlink transmission rate variation patterns, a new video flow feature M value probability distribution, based on downlink byte rate variation is proposed, and video classification is realized by Support Vector Machine (SVM). The experimental results show that the probability distribution of M value is a better feature for classification of six kinds of common network video applications than other commonly used flow features.
- Network video /
- Traffic classification /
- Probability distribution of M value

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參考文獻(24)

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