MAppGraph: Mobile-App Classification on Encrypted Network Traffic using Deep Graph Convolution Neural Networks
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2021-12-06
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
14
1025–1038
1025–1038
Series
Annual Computer Security Applications Conference, ACSAC 2021
Abstract
Identifying mobile apps based on network traffic has multiple benefits for security and network management. However, it is a challenging task due to multiple reasons. First, network traffic is encrypted using an end-to-end encryption mechanism to protect data privacy. Second, user behavior changes dynamically when using different functionalities of mobile apps. Third, it is hard to differentiate traffic behavior due to common shared libraries and content delivery within modern mobile apps. Existing techniques managed to address the encryption issue but not the others, thus achieving low detection/classification accuracy. In this paper, we present MAppGraph, a novel technique to classify mobile apps, addressing all the above issues. Given a chunk of traffic generated by an app, MAppGraph constructs a communication graph whose nodes are defined by tuples of IP address and port of the services connected by the app, edges are established by the weighted communication correlation among the nodes. We extract information from packet headers without analyzing encrypted payload to form feature vectors of the nodes. We leverage deep graph convolution neural networks to learn the diverse communication behavior of mobile apps from a large number of graphs and achieve a fast classification. To validate our technique, we collect traffic of a hundred mobile apps on the Android platform and run extensive experiments with various experimental scenarios. The results show that MAppGraph significantly improves classification accuracy by up to 20% compared to recently developed techniques and demonstrates its practicality for security and network management of mobile services.Description
Keywords
Other note
Citation
Pham, T-D, Ho, T-L, Truong-Huu, T, Cao, T-D & Truong, L 2021, MAppGraph: Mobile-App Classification on Encrypted Network Traffic using Deep Graph Convolution Neural Networks . in Proceedings - 37th Annual Computer Security Applications Conference, ACSAC 2021 . ACM, pp. 1025–1038, Annual Computer Security Applications Conference, Virtual, online, 06/12/2021 . https://doi.org/10.1145/3485832.3485925
