TrCMP: An App Usage Inference Method for Mobile Service Enhancement
In order to improve the quality of life and the efficiency of work, users need timely and accurate services provided by mobile devices. However, for the same service, different users have various personalized use styles, such as usage time, invoking frequency, etc. As a result, the accuracy of real-time service recommendations often depends on effective user behavior analysis. Technically, user behaviors associated with a certain service could be reflected with traffic, CPU, memory and energy consumption during app running. In this paper, an app usage inference method, named TrCMP, is investigated. This method takes Traffic, CPU, Memory and Power into consideration in a comprehensive way for analyzing user behaviors. Extensive experiments are conducted to validate the efficiency and effectiveness of our method.
KeywordsApp usage Behavior Service enhancement Android
This work is supported in part by the National Science Foundation of China under Grant No. 61672276, the National Key Research and Development Program of China under Grant No. 2017YFB1400600, and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University.
- 1.Xu, Q., et al.: Automatic generation of mobile app signatures from traffic observations. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 1481–1489 (2015)Google Scholar
- 2.Taylor, V.F., Spolaor, R., Conti, M., Martinovic, I.: AppScanner: automatic fingerprinting of smartphone apps from encrypted network traffic. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 439–454 (2016)Google Scholar
- 3.Chen, Y., Jin, X., Sun, J., Zhang, R., Zhang, Y.: POWERFUL: mobile app fingerprinting via power analysis. In: IEEE INFOCOM 2017, IEEE Conference on Computer Communications, pp. 1–9 (2017)Google Scholar
- 5.Dai, S., Tongaonkar, A., Wang, X., Nucci, A., Song, D.: NetworkProfiler: towards automatic fingerprinting of android apps. In: 2013 Proceedings IEEE INFOCOM, pp. 809–817 (2013)Google Scholar
- 6.Zhou, X., et al.: Identity, location, disease and more: inferring your secrets from android public resources, pp. 1017–1028 (2013)Google Scholar
- 9.Khomh, F., Yuan, H., Zou, Y.: Adapting Linux for mobile platforms: an empirical study of Android. In: 2012 28th IEEE International Conference on Software Maintenance (ICSM), pp. 629–632 (2012)Google Scholar
- 11.Shehu, Z., Ciccotelli, C., Ucci, D., Aniello, L., Baldoni, R.: Towards the usage of invariant-based app behavioral fingerprinting for the detection of obfuscated versions of known malware. In: International Conference on Next Generation Mobile Applications, Security and Technologies, pp. 121–126 (2016)Google Scholar
- 12.Yang, L., Yuan, M., Wang, W., Zhang, Q., Zeng, J.: Apps on the move: a fine-grained analysis of usage behavior of mobile apps. In: 35th Annual IEEE International Conference on Computer Communications, pp. 1–9 (2016)Google Scholar