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This study introduces a Deep Neural Network (DNN) framework to identify mobile app activities from encrypted traffic. It effectively distinguishes known activities and flags unknown ones, enhancing mobile security.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Mobile users face privacy risks despite encrypted communications.
  • Identifying user activities within encrypted mobile traffic is challenging due to vast application numbers.

Purpose of the Study:

  • To propose a novel Deep Neural Network (DNN) framework for fine-grained in-app activity detection.
  • To address the challenge of training DNNs with limited application data by filtering unknown data.

Main Methods:

  • A DNN-based user activity detection framework is developed.
  • A time window-based approach segments traffic flow for activity identification.
  • DNN output layer probability distribution is used to filter unknown application data.

Main Results:

  • The framework achieved over 90% accuracy in identifying previously trained in-app activities.
  • It demonstrated an average accuracy of 79% in classifying previously untrained in-app activity traffic as unknown data.

Conclusions:

  • The proposed DNN framework effectively identifies in-app activities from encrypted traffic.
  • It successfully distinguishes known activities and identifies unknown ones, improving mobile security and privacy.