Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 11
3353-3358

Comprehensive research on multiple feature sets and multiple label sets for mobile network traffic classification

Huang Yi1
Liu Zhen1
Wang Ruoyu2,3
Chen Jietong1
1. School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China
2. Research Center of Information & Network Engineering, South China University of Technology, Guangzhou 510006, China
3. Communication & Computer Network Lab of Guangdong, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2019.05.0255

Abstract

Mobile traffic classification/clustering is an important foundation for mobile network traffic management. However, the mobile network traffic data used by different papers were collected from different network environment. In addition, the labels and the flow statistical features of mobile traffic were different from papers. These experimental results couldn't be directly compared. This paper collected the traffic data generated by App based on MobileGT system. The two kinds of labels were built on these data(App level and function level), and two kinds of flow statistical features were independently extracted on these traffic data. This paper comprehensively researched the machine learning techniques on the traffic data with different labels and different flow statistical features. The experimental results show that the uni-direction flow based features are better than bidirection flow based features, random forest and AdaBoost are better on classifying mobile traffic data, and K-means is better on clustering mobile traffic data.

Foundation Support

国家自然科学基金资助项目(61501128)
广东省自然科学基金资助项目(2017A030313345)
国家级大学生创新创业训练计划资助项目(201710573005,S202010573042)
中央高校基本业务费资助项目(x2rj/D2174870)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.05.0255
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 11
Section: Algorithm Research & Explore
Pages: 3353-3358
Serial Number: 1001-3695(2020)11-032-3353-06

Publish History

[2020-11-05] Printed Article

Cite This Article

黄燚, 刘珍, 王若愚, 等. 移动互联网流量分类的多特征集合和多类别标签研究 [J]. 计算机应用研究, 2020, 37 (11): 3353-3358. (Huang Yi, Liu Zhen, Wang Ruoyu, et al. Comprehensive research on multiple feature sets and multiple label sets for mobile network traffic classification [J]. Application Research of Computers, 2020, 37 (11): 3353-3358. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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