| dc.description.abstract |
The volume of traffic belonging to mobile applications over the Internet has already crossed the traffic generated due to traditional desktop-based Internet browsing. To protect security and privacy of the mobile user, many smartphone applications use encryption to encapsulates communications over the Internet. However it is not possible to decode the actual message contents, even then the statistical information present in the traffic is useful to identify application and the associated activity. This paper proposes a machine learning based framework to analyze the encrypted mobile traffic with the objective of finding the mobile application usage patterns of smartphone users. This information can be used by various authorities to profile mobile users with respect to their age, gender profession, etc. which would further help them to look for any suspicious or anomalous behaviour. Proposed framework has been tested on the network traffic data pertaining to popular smartphone applications such as GMail, Facebook, and Youtube and have achieved 97.46% accuracy for application identification. Further, the proposed framework also achieved a fairly high accuracy, 77.37% when used for the classification of exact activity performed by the smartphone user in different applications |
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