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Identifying In-App User Actions from Mobile Web Logs

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Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10938))

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Abstract

We address the problem of identifying in-app user actions from Web access logs when the content of those logs is both encrypted (through HTTPS) and also contains automated Web accesses. We find that the distribution of time gaps between HTTPS accesses can distinguish user actions from automated Web accesses generated by the apps, and we determine that it is reasonable to identify meaningful user actions within mobile Web logs by modelling this temporal feature. A real-world experiment is conducted with multiple mobile devices running some popular apps, and the results show that the proposed clustering-based method achieves good accuracy in identifying user actions, and outperforms the state-of-the-art baseline by \(17.84\%\).

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Notes

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Acknowledgments

This research is supported by LPDP (Indonesia Endowment Fund for Education) and a Linkage Project grant of the Australian Research Council (LP120200413).

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Authors and Affiliations

  1. School of Science, Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Bilih Priyogi, Mark Sanderson, Flora Salim, Jeffrey Chan & Yongli Ren

  2. Department of Infrastructure Engineering, University of Melbourne, Melbourne, Australia

    Martin Tomko

Authors
  1. Bilih Priyogi
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  2. Mark Sanderson
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  3. Flora Salim
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  4. Jeffrey Chan
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  5. Martin Tomko
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  6. Yongli Ren
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Corresponding author

Correspondence to Bilih Priyogi .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Priyogi, B., Sanderson, M., Salim, F., Chan, J., Tomko, M., Ren, Y. (2018). Identifying In-App User Actions from Mobile Web Logs. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10938. Springer, Cham. https://doi.org/10.1007/978-3-319-93037-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-93037-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93036-7

  • Online ISBN: 978-3-319-93037-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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