Contact Project Developer Ashish D. Tiwari [astiwz@gmail.com]
Download Synopsis Abstract
Websites Mobile Apps C#.NET ASP.NET BE-Engineering(CO/IT) ME-Engineering(CO/IT) BCS MCS BCA MCA MCM BSC Computer/IT MSC Computer/IT Diploma (CO/IT) IEEE-2016

Service Usage Classification with Encrypted Internet Traf?c in Mobile Messaging Apps

Service Usage Classi?cation with Encrypted Internet Traf?c in Mobile Messaging Apps
Abstract-Synopsis-Documentation

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

Existing System

A hierarchical segmentation based on the definitions of session and dialog: we first segment each traffic-flow into sessions using a thresholding method;  then we segment each session into dialogs by a bottom-up hierarchical clustering based method mixed with thresholding heuristics. The above method can segment the raw Internet traffic into dialogs. Note that most dialogs contain single usage type, and only a few are the mixture of multiple usage types.

Proposed System

we developed a system for classifying service usages using encrypted Internet traffic in mobile messaging Apps by jointly modeling behavior structure, network traffic characteristics, and temporal dependencies. There are four modules in our system including traffic segmentation, traffic feature extraction, service usage prediction, and outlier detection and handling. Specifically, we first built a data collection platform to collect the traffic-flows of in-App usages and the corresponding usage types reported by mobile users. We then hierarchically segment these traffic from traffic-flows to sessions to dialogs where each is assumed to be of individual usage or mixed usages. Also, we extracted the packet length related features and the time delay related features from traffic-flows to prepare the training data. In addition, we learned service usage classifiers to classify these segmented dialogs. Moreover, we detected the anomalous dialogs with mixed usages and segmented these mixed dialogs into multiple sub-dialogs of singletype usage. Finally, the experimental results on real world WeChat and WhatsApp traffic data demonstrate the performances of the proposed method. With this system, we showed that the valuable applications for in-App usage analytics can be enabled to score quality of experiences, profile user behaviors and enhance customer care


Comment is Only Available for registered users! Create Account or Login Now!