Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps

Abstract

Service Usage Classification with Encrypted Recent years have witnessed the increasing popularity of mobile messaging apps such as WeChat and WhatsApp.In fact, messaging apps have become the hubs for most mobile user activities.Service Usage Classification with Encrypted Internet Traffic Messaging apps, for example, help people text each other, share photos, chat, and engage in commercial activities such as bill payment, ticket shopping.

Mobile businessesmonetizetheirservices in messaging apps. Service usage analytics in messaging apps therefore becomes critical for business as it can help understand end-user in-app behaviors and thus enable a variety of applications.

Conclusion

In this Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps paper, we developed a system for classifying service uses using encrypted Internet traffic in mobile messaging apps by jointly modeling behavioral structure, network traffic characteristics and temporary dependencies.

Projects on Service Usage Classification Our system contains four modules including traffic segmentation, extraction of traffic features, prediction of service usage, and outer detection and handling.Also, to prepare the training data, we extracted the packet length related features and the time delay related features from traffic flows. We also learned service usage classifiers to classify these segmented dialogs.

In addition, projects on Service Usage Classification we detected the anomalous dialogs with mixed uses and segmented these mixed dialogs into multiple single-type usage subdialogs. Finally, WeChat and WhatsApp real world experimental results show the performance of the proposed method. With this system, we have shown that valuable applications for in-app usage analytics can enable experiences to be scored, user behaviors profiled, and customer care improved.