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%.
CITATION STYLE
Priyogi, B., Sanderson, M., Salim, F., Chan, J., Tomko, M., & Ren, Y. (2018). Identifying in-app user actions from mobile web logs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10938 LNAI, pp. 300–311). Springer Verlag. https://doi.org/10.1007/978-3-319-93037-4_24
Mendeley helps you to discover research relevant for your work.