An Optimized K-means Clustering for Improving Accuracy in Traffic Classification

被引：0
作者：
Shasha Zhao
Yi Xiao
Yueqiang Ning
Yuxiao Zhou
Dengying Zhang
机构：
[1] Nanjing University of Posts and Telecommunications,College of Internet of Things
[2] Nanjing University of Posts and Telecommunications,College of Telecommunications, Information Engineering
[3] Nanjing University of Posts and Telecommunications,Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things
来源：
Wireless Personal Communications | 2021年 / 120卷
关键词：
SOM; K-means; Traffic classification; Feature selection;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
With the explosive grown network traffic, the traditional port- and payload-based methods are insatiable for the requirements of privacy protection as well as the fast real-time classification for the today traffic classification. Here, a network traffic classification model based on both the Self-Organizing Maps (SOM) and the K-means fusion algorithm is proposed. In which, the traffic data is initially clustered by the SOM network to derive the cluster number and each cluster center value. Then those values are taken as the initial parameters to run the K-means algorithm, achieving optimal classification. As results compared with the traditional K-means algorithm, the initially clustering done by using the SOM network not only inherits its advantages of simple method and efficient processing, but also reduces time cost. Moreover, a significant improvement in coossification accuracy is achieved with our proposed algorithm.
引用
收藏
页码：81 / 93
页数：12
相关论文
共 41 条
  • [1] Nahum CV(2020)Testbed for 5G connected artificial intelligence on virtualized networks IEEE Access 8 223202-223213
  • [2] Tzanakaki A(2017)Wireless-optical network convergence: Enabling the 5G architecture to support operational and end-user services IEEE Communications Magazine 55 184-192
  • [3] Anastasopoulos M(2020)Encrypted network traffic classification using deep and parallel network-in-network models IEEE Access 8 132950-132959
  • [4] Berberana I(2019)Mobile encrypted traffic classification using deep learning: Experimental evaluation, lessons learned, and challenges IEEE Transactions on Network and Service Management 16 445-458
  • [5] Syrivelis D(2019)A survey of techniques for mobile service encrypted traffic classification using deep learning IEEE Access 7 54024-54033
  • [6] Flegkas P(2016)Traffic classification for managing applications networking profiles Security and Communication Networks 9 2557-2575
  • [7] Bu Z(2020)FPGA-based network traffic classification using machine learning IEEE Access 8 175637-175650
  • [8] Zhou B(2019)Towards the deployment of machine learning solutions in network traffic classification: A systematic survey IEEE Communications Surveys and Tutorials 21 1988-2014
  • [9] Cheng P(2007)Bayesian neural networks for internet traffic classification IEEE Transactions on Neural Networks 18 223-239
  • [10] Zhang K(2015)Recent advancement in machine learning based Internet traffic classification Procedia Computer Science 60 784-791
12345