Understanding Mobile App Usage Patterns Using In-App Advertisements
Recent years have seen an explosive growth in the number of mobile devices such as smart phones and tablets. This has resulted in a growing need of the operators to understand the usage patterns of the mobile apps used on these devices. Previous studies in this area have relied on volunteers using instrumented devices or using fields in the HTTP traffic such as User-Agent to identify the apps in network traces. However, the results of the former approach are difficult to be extrapolated to real-world scenario while the latter approach is not applicable to platforms like Android where developers generally use generic strings, that can not be used to identify the apps, in the User-Agent field. In this paper, we present a novel way of identifying Android apps in network traces using mobile in-app advertisements. Our preliminary experiments with real world traces show that this technique is promising for large scale mobile app usage pattern studies. We also present an analysis of the official Android market place from an advertising perspective.
KeywordsUsage Pattern Android Platform Content Distribution Network Network Trace Cellular Service Provider
Unable to display preview. Download preview PDF.
- 3.Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., Venkataraman, S.: Identifying diverse usage behaviors of smartphone apps. In: Proceedings of the 11th Internet Measurement Conference, IMC (2011)Google Scholar
- 4.Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., Estrin, D.: A first look at traffic on smartphones. In: Proceedings of the 10th Internet Measurement Conference, IMC (2010)Google Scholar
- 6.Wei, X., Gomez, L., Neamtiu, I., Faloutsos, M.: Profiledroid: Multi-layer profiling of android applications. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, MobiCom (2012)Google Scholar
- 7.Aioffi, W.M., Mateus, G.R., Almeida, J.M., Mendes, D.S.: Mobile dynamic content distribution networks. In: Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM (2004)Google Scholar
- 8.Grace, M.C., Zhou, W., Jiang, X., Sadeghi, A.R.: Unsafe exposure analysis of mobile in-app advertisements. In: Proceedings of the 5th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WISEC 2012 (2012)Google Scholar
- 9.Pearce, P., Felt, A.P., Nunez, G., Wagner, D.: Addroid: Privilege separation for applications and advertisers in android. In: Proceedings of the 7th ACM Symposium on Information, Computer and Communications Security, ASIACCS (2012)Google Scholar
- 10.Leontiadis, I., Efstratiou, C., Picone, M., Mascolo, C.: Don’t kill my ads!: Balancing privacy in an ad-supported mobile application market. In: Proceedings of the 13th Workshop on Mobile Computing Systems and Applications, HotMobile (2012)Google Scholar
- 11.Vallina-Rodriguez, N., Shah, J., Finamore, A., Grunenberger, Y., Papagiannaki, K., Haddadi, H., Crowcroft, J.: Breaking for commercials: Characterizing mobile advertising. In: Proceedings of the 12th Internet Measurement Conference, IMC (2012)Google Scholar
- 13.Dai, S., Tongaonkar, A., Wang, X., Nucci, A., Song, D.: Networkprofiler: Towards automatic fingerprinting of android apps. In: Proceedings of the 32nd IEEE International Conference on Computer Communications, INFOCOM (2013)Google Scholar