Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile Application Traffic

IF 2 3区计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Supported Cooperative Work-The Journal of Collaborative Computing Pub Date : 2023-05-24 DOI:10.1109/CSCWD57460.2023.10152687

Qingya Yang, Peipei Fu, Junzheng Shi, Bingxu Wang, Zhuguo Li, G. Xiong

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引用次数: 0

Abstract

With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. Therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. Therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. Then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN.

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基于多特征融合的移动应用加密流量分类方法

随着移动互联网的快速发展，出现了大量的移动应用，移动互联网流量出现了大爆炸。因此，为了更有效地管理移动互联网流量，需要对应用流量进行准确的分类。但是，移动应用流量的加密逐渐淘汰了传统的基于特定签名的分类方法，大大增加了移动应用流量的分类难度。为此，我们提出了一种基于多特征融合(MFF)的移动应用流量分类方法。我们还提取了数据包长度序列、字节序列、统计特征等。然后，基于Relief-F算法对特征进行加权融合，得到最优特征集;最后，我们使用机器学习技术进行应用分类。与其他几种特征提取方法相比，MFF在16种移动应用中取得了出色的性能，准确率达到97.6%，在vpn -非vpn中f1得分超过99%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Supported Cooperative Work-The Journal of Collaborative Computing COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

6.40

自引率

4.20%

发文量

审稿时长

>12 weeks

期刊介绍： Computer Supported Cooperative Work (CSCW): The Journal of Collaborative Computing and Work Practices is devoted to innovative research in computer-supported cooperative work (CSCW). It provides an interdisciplinary and international forum for the debate and exchange of ideas concerning theoretical, practical, technical, and social issues in CSCW. The CSCW Journal arose in response to the growing interest in the design, implementation and use of technical systems (including computing, information, and communications technologies) which support people working cooperatively, and its scope remains to encompass the multifarious aspects of research within CSCW and related areas. The CSCW Journal focuses on research oriented towards the development of collaborative computing technologies on the basis of studies of actual cooperative work practices (where ‘work’ is used in the wider sense). That is, it welcomes in particular submissions that (a) report on findings from ethnographic or similar kinds of in-depth fieldwork of work practices with a view to their technological implications, (b) report on empirical evaluations of the use of extant or novel technical solutions under real-world conditions, and/or (c) develop technical or conceptual frameworks for practice-oriented computing research based on previous fieldwork and evaluations.