[1]姚永生,董育宁,邱晓晖.基于相似性度量的网络流分类模型融合[J].计算机技术与发展,2021,31(12):7-12.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 002]
　YAO Yong-sheng,DONG Yu-ning,QIU Xiao-hui.Network Traffic Classification Model Fusion Based on Similarity Measurement[J].,2021,31(12):7-12.[doi:10. 3969 / j. issn. 1673-629X. 2021. 12. 002]
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基于相似性度量的网络流分类模型融合()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年12期
页码:
7-12
栏目:
人工智能
出版日期:
2021-12-10

文章信息/Info

Title:
Network Traffic Classification Model Fusion Based on Similarity Measurement
文章编号:
1673-629X(2021)12-0007-06
作者:
姚永生; 董育宁; 邱晓晖
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
YAO Yong-sheng; DONG Yu-ning; QIU Xiao-hui
School of Telecommunications & Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
Jensen-Shannon 距离; 迁移学习; 机器学习; 网络流分类; 概念漂移
Keywords:
Jensen-Shannon distance; transfer learning; machine learning; network traffic classification; conceptual drift
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 12. 002
摘要:
由于网络流特征会随时间和网络环境的变化而发生概念漂移,不同类别应用的流发生漂移情况不同,导致基于机器学习的流量分类方法精度明显降低。 同时,随着互联网网络技术的不断提高,使得过去采集并做好标签的大量视频流样本数据会发生很大变化,导致可用的训练集较少,需要实时采集和标注大量的新数据。 针对上述问题,提出一种结合Jensen-Shannon 距离、MultiTrAdaBoost 和 RandomForest 算法的分类方法。 该方法的核心思想是:度量新老视频数据流之间的相似性,根据度量结果判断采用何种模型进行分类,其中的迁移学习分类方法是从老数据集中选出有用信息的样本来辅助新数据集样本的识别与分类。 文中新老数据集样本特征属性分布是不一样的。 实验结果表明,与现有的方法比较,该方法可以更好地实现典型的网络视频流分类,表现出较好的分类性能和泛化能力( 即,模型的总体准确率标准差较小) 。
Abstract:
Because network flow characteristics will experience conceptual drift with time and network environment changes,the flow of different types? ?of applications drifts differently,resulting in a significant reduction in the accuracy of the traffic classification method based on machine learning. Meanwhile,with the continuous improvement of Internet network technology, the large number of video streams ample data collected and labeled in the past will change greatly,resulting in fewer training sets available,and a large amount of new data needs to be collected and labeled in real time. Regarding the problem above, a classification method combining Jensen-Shannon distance,MultiTrAdaBoost and Randon Forest algorithms is proposed. The core idea of this method is to measure the similarity between the new and old video data streams,and determine which model to use for classification based on the measurement results. The migration learning classification method is to select useful information samples from the old data set to assist the identification and classification of the new data set samples. In the article,the distribution of feature attributes of the new and old data sets is different. Experiment shows that compared with the existing methods,the proposed method can better implement typical network video stream classification,showing better classification performance and generalization ability ( that is,the overall accuracy of the model has a smaller standard deviation) .

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更新日期/Last Update: 2021-12-10