Mobile Traffic Application Recognition Based on Multi-Feature Fusion

doi:10.3969/j.issn.1671-1122.2022.07.003

Abstract

Abstract:

Mobile application recognition is a key technology in the research field of mobile network security and management. Aiming at the failure of manual feature extraction after mobile applications update and insufficient feature extraction, this paper proposed a new traffic-based mobile application recognition model called MAITSF. The model adopted a multi-channel parallel architecture. In this model, the convolutional neural network (CNN) was used to extract the spatial characteristics of mobile application traffic, and the long short-term memory (LSTM) network was used to extract the temporal characteristics, and the features extracted from each channel were fused. On this basis, a channel attention module was introduced to allocate a series of weight parameters, so that the model can focus more on the key features extracted by the neural network, and enhance the ability of traffic characteristics characterization. In this paper, comparative experiments were carried out on the public dataset (CIC-AAGM2017) and the actual dataset collected in the laboratory. The experimental results show that the classification accuracy of MAITSF on the above two datasets reached 98%, which is more than 4% higher than the existing typical models.

Key words: mobile application, convolutional neural network, long short-term memory network, feature fusion, channel attention model

CLC Number:

TP309

LIU Guangjie, DUAN Kun, ZHAI Jiangtao, QIN Jiayu. Mobile Traffic Application Recognition Based on Multi-Feature Fusion[J]. Netinfo Security, 2022, 22(7): 18-26.

Figures/Tables 14

References 21

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公开数据集	数据量/条	比例	实际数据集	数据量/条	比例
ILoveHue	43473	11.59%	12306	34192	7.46%
Video call	46866	12.51%	百度地图	50990	11.12%
Bookmate	37539	10.02%	大麦网	57933	12.53%
Medicalid Free	30285	8.08%	大众点评	53352	11.52%
Lifehack Cheatsheet:A lifehacker app	55612	14.83%	饿了么	51455	11.22%
Instant Gaming	28017	7.48%	返利网	42568	9.28%
Knight Dark Gothic Wallpaper	35540	9.48%	马蜂窝	36259	7.91%
Apex Launcher	34448	9.19%	美团外卖	38438	8.61%
Access Phone	28145	7.51%	亚马逊	45622	9.94%
Freedom Mobile My Count	34861	9.31%	前程无忧	47720	10.41%
总计	374786	100%	—	458529	100%

公开数据集	精确率	召回率	F1
ILoveHue	0.93	0.98	0.97
Video call	1.00	1.00	1.00
Bookmate	0.98	0.95	0.95
Medicalid Free	1.00	1.00	1.00
Lifehack Cheatsheet:A lifehacker app	1.00	1.00	1.00
Instant Gaming	1.00	0.98	1.00
Knight Dark Gothic Wallpaper	0.95	0.92	0.93
Apex Launcher	0.98	1.00	0.99
Access Phone	1.00	0.97	1.00
Freedom Mobile My Count	0.92	0.98	0.93
实际数据集	精确率	召回率	F1
12306	0.98	1.00	0.99
百度地图	0.99	0.99	1.00
大麦网	1.00	0.98	0.99
大众点评	0.98	1.00	0.99
饿了么	1.00	1.00	1.00
返利网	1.00	0.99	1.00
马蜂窝	0.95	0.96	0.95
美团外卖	1.00	1.00	1.00
亚马逊	0.99	0.99	0.99
前程无忧	0.95	0.94	0.95

方法	准确率	精确率	召回率	F1
文献[14]方法	0.908	0.918	0.908	0.910
文献[21]方法	0.926	0.909	0.926	0.914
本文方法	0.976	0.972	0.976	0.972

方法	准确率	精确率	召回率	F1
文献[14]方法	0.914	0.921	0.914	0.915
文献[21]方法	0.931	0.935	0.931	0.924
本文方法	0.985	0.947	0.985	0.966