Service usage classification with encrypted internet traffic in mobile messaging apps

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The document presents a project focused on classifying encrypted internet traffic in mobile messaging apps to enhance network management and service quality. It introduces a system called CUMMA that analyzes user behavior and traffic characteristics to identify in-app usages despite the challenges posed by secure protocols like HTTPS. Additionally, it outlines hardware and software requirements for implementing the proposed classification method.

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Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps
Abstract
The rapid adoption of mobile messaging Apps has enabled us to collect massive amount
of encrypted Internet traffic of mobile messaging. The classification of this traffic into different
types of in-App service usages can help for intelligent network management, such as managing
network bandwidth budget and providing quality of services. Traditional approaches for
classification of Internet traffic rely on packet inspection, such as parsing HTTP headers.
However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to
transmit data. This imposes significant challenges on the performances of service usage
classification by packet inspection. To this end, in this paper, we investigate how to exploit
encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system,
named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling
user behavioral patterns, network traffic characteristics and temporal dependencies. Along this
line, we first segment Internet traffic from traffic-flows into sessions with a number of dialogs in
a hierarchical way. Also, we extract the discriminative features of traffic data from two
perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to
classify these segmented dialogs into single-type usages or outliers. In addition, we design a
clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers
and decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA enables
mobile analysts to identify service usages and analyze end-user in-App behaviors even for
encrypted Internet traffic. Finally, the extensive experiments on real-world messaging data
demonstrate the effectiveness and efficiency of the proposed method for service usage
classification.

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