Abstract
Mobile traffic has accounted for a principal part of network traffic with the ways of accessing the Internet shifting to mobile devices. Subsequently, mobile traffic analysis becomes the focus of research. However, there is a lack of studies on mobile traffic characterization, while the traffic characteristics are important for obtaining a clear understanding of mobile networks. To make up for this research gap, this paper provides a comprehensive overview and characterization of current mobile application traffic. The properties of mobile traffic are described from four perspectives based on a large-scale mobile traffic dataset, including basic information (the destination IP, the destination Port, and the protocol), domain name usage, HTTP/TLS protocol usage, and the traffic flow. Besides, the properties of mobile traffic shown by different application categories are also analyzed simultaneously. Finally, a discussion is provided on how the presented observations work on various traffic analysis tasks in mobile networks.
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References
Statista Research Department. Mobile app usage - Statistics & Facts. https://www.statista.com/topics/1002/mobile-app-usage/
Buildfire. Mobile app Download Statistics & Usage Statistics (2021) . https://buildfire.com/app-statistics/
First Site Guide. Mobile Web Traffic Stats and Facts in 2021. https://firstsiteguide.com/mobile-traffic-stats/
Conti, M., Li, Q.Q., Maragno, A., Spolaor, R.: The dark side(-channel) of mobile devices: a survey on network traffic analysis. IEEE Commun. Surv. Tutor. 20(4), 2658–2713 (2018). https://doi.org/10.1109/COMST.2018.2843533
Trinh, H.D., Bui, N., Widmer, J., Giupponi, L., Dini, P.: Analysis and modeling of mobile traffic using real traces. In: IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, pp. 1–6 (2017). https://doi.org/10.1109/PIMRC.2017.8292200
Shi, H., Li, Y.: Discovering periodic patterns for large scale mobile traffic data: method and applications. IEEE Trans. Mob. Comput. 17(10), 2266–2278 (2018). https://doi.org/10.1109/TMC.2018.2799945
Fang, C., Liu, J., Lei, Z.: Fine-grained HTTP web traffic analysis based on large-scale mobile datasets. IEEE Access 4, 4364–4373 (2016). https://doi.org/10.1109/ACCESS.2016.2597538
Wang, R., Liu, Z., Cai, Y., Tang, D., Yang, J., Yang, Z.: Benchmark data for mobile app traffic research. In: 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 402–411 (2018). https://doi.org/10.1145/3286978.3287000
Aceto, G., Ciuonzo, D., Montieri, A., Persico, V., Pescape, A.: MIRAGE: mobile-app traffic capture and ground-truth creation. In: International Conference on Computing, Communications and Security, pp. 1–8 (2019). https://doi.org/10.1109/CCCS.2019.8888137
Rezaei, S., Kroencke, B., Liu, X.: Large-scale mobile app identification using deep learning. IEEE Access 8, 348–362 (2019)
Sengupta, S., Ganguly, N., De, P., Chakraborty, S.: Exploiting diversity in android TLS implementations for mobile app traffic classification. In: World Wide Web Conference, pp. 1657–1668 (2019). https://doi.org/10.1145/3308558.3313738
Chen, Y., Zang, T., Zhang, Y., Zhou, Y., Wang, Y.: Rethinking encrypted traffic classification: a multi-attribute associated fingerprint approach. In: IEEE 27th International Conference on Network Protocols, pp. 1–11 (2019). https://doi.org/10.1109/ICNP.2019.8888043
Wang, X., Chen, S., Jinshu, S.: Real network traffic collection and deep learning for mobile app identification. Wirel. Commun. Mob. Comput. 2020, 1–14 (2020). https://doi.org/10.1155/2020/4707909
NUDT_MobileTraffic. https://github.com/Abby-ZS/NUDT_MobileTraffic
Aceto, G., Ciuonzo, D., Montieri, A., Pescape, A.: Mobile encrypted traffic classification using deep learning: experimental evaluation, lessons learned, and challenges. IEEE Trans. Netw. Serv. Manag. 16(2), 445–458 (2019). https://doi.org/10.1109/TNSM.2019.2899085
Transport Layer Security (TLS) Parameters. https://www.iana.org/assignments/tls-parameters/tls-parameters.xhtml#tls-parameters-4
Bub, D., Hartmann, L., Bozakov, Z., Wendzel, S.: Towards passive identification of aged android devices in the home network. In: Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference, pp. 17–20 (2022). https://doi.org/10.1145/3528580.3528584
Almashhadani, A.O., Kaiiali, M., Carlin, D., Sezer, S.: MaldomDetection: a system for detecting algorithmically generated domain names with machine learning. Comput. Secur. 93(2020), 1–13 (2020)
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Appendix A
Table 6 provides the cipher suites mentioned in this paper.
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Zhao, S., Zhong, J., Chen, S., Liang, J. (2022). Comprehensive Mobile Traffic Characterization Based on a Large-Scale Mobile Traffic Dataset. In: Yuan, X., Bai, G., Alcaraz, C., Majumdar, S. (eds) Network and System Security. NSS 2022. Lecture Notes in Computer Science, vol 13787. Springer, Cham. https://doi.org/10.1007/978-3-031-23020-2_12
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