Conference Proceedings

Identifying In-App User Actions from Mobile Web Logs

Bilih Priyogi, Mark Sanderson, Flora Salim, Jeffrey Chan, Martin Tomko, Yongli Ren, D Phung (ed.), VS Tseng (ed.), GI Webb (ed.), B Ho (ed.), M Ganji (ed.), L Rashidi (ed.)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER INTERNATIONAL PUBLISHING AG | Published : 2018

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-..

View full abstract

University of Melbourne Researchers

Grants

Awarded by Linkage Project grant of the Australian Research Council


Funding Acknowledgements

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