Document Type
Article
Publication Date
2019
Publication Source
IEEE Access
Abstract
The rapid adoption of mobile devices has dramatically changed the access to various net- working services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted features, and frequent features update. With the excellent ability of automatic feature learning, Deep Learning (DL) undoubtedly becomes a highly desirable approach for mobile services traffic classification, especially encrypted traffic. This survey paper looks at emerging research into the application of DL methods to encrypted traffic classification of mobile services and presents a general framework of DL-based mobile encrypted traffic classification. Moreover, we review most of the recent existing work according to dataset selection, model input design, and model architecture. Furthermore, we propose some noteworthy issues and challenges about DL-based mobile services traffic classification.
Inclusive pages
54024-54033
ISBN/ISSN
2169-3536
Document Version
Published Version
Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Volume
7
Peer Reviewed
yes
Sponsoring Agency
National Natural Science Foundation of China (NSFC) ; Natural Science Foundation of Jiangsu Province
eCommons Citation
Wang, Pan; Chen, Xuejiao; Ye, Feng; and Sun, Zhixin, "A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning" (2019). Electrical and Computer Engineering Faculty Publications. 477.
https://ecommons.udayton.edu/ece_fac_pub/477
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/ACCESS.2019.2912896