计算机科学
交通分类
字节
加密
互联网
特征提取
特征(语言学)
数据挖掘
网络数据包
人工智能
互联网流量
移动计算
移动设备
移动互联网
计算机网络
万维网
语言学
哲学
操作系统
作者
Qingya Yang,Peipei Fu,Junzheng Shi,Bingxu Wang,Zhen Li,Gang Xiong
标识
DOI:10.1109/cscwd57460.2023.10152687
摘要
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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