International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Discernment of Android User Interaction Data Distribution Using Deep Learning

  • Ho, Jun-Won (Department of Information Security, Seoul Women's University)
  • Received : 2022.06.17
  • Accepted : 2022.06.24
  • Published : 2022.08.31
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Abstract

In this paper, we employ deep neural network (DNN) to discern Android user interaction data distribution from artificial data distribution. We utilize real Android user interaction trace dataset collected from [1] to evaluate our DNN design. In particular, we use sequential model with 4 dense hidden layers and 1 dense output layer in TensorFlow and Keras. We also deploy sigmoid activation function for a dense output layer with 1 neuron and ReLU activation function for each dense hidden layer with 32 neurons. Our evaluation shows that our DNN design fulfills high test accuracy of at least 0.9955 and low test loss of at most 0.0116 in all cases of artificial data distributions.

Keywords

Acknowledgement

This work utilizes Android user interaction trace dataset collected from [1] and it is approved by IRB in Seoul Women's University. This work was supported by a research grant from Seoul Women's University (2022-0137). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1057019).

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