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TrCMP: An App Usage Inference Method for Mobile Service Enhancement

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11342))

Abstract

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.

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Notes

  1. 1.

    http://www.wandoujia.com/apps.

  2. 2.

    https://play.google.com/store.

References

  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 

  4. Taylor, V.F., Spolaor, R., Conti, M., Martinovic, I.: Robust smartphone app identification via encrypted network traffic analysis. IEEE Trans. Inf. Forensics Secur. 13(1), 63–78 (2018)

    Article  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 

  7. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Article  Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

    Article  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 

  10. Liu, X., Li, H., Lu, X., Xie, T., Mei, Q., Feng, F., Mei, H.: Understanding diverse usage patterns from large-scale appstore-service profiles. IEEE Trans. Softw. Eng. 44(4), 384–411 (2018)

    Article  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 

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Acknowledgments

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.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Xuan Zhao, Hongli Nie, Wajid Rafique & Wanchun Dou

  2. Department of Computer and Information Sciences, Fordham University, New York, 10458, USA

    Md Zakirul Alam Bhuiyan

  3. School of Information Science and Engineering, Qufu Normal University, Qufu, 276826, China

    Lianyong Qi

Authors
  1. Xuan Zhao
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  2. Md Zakirul Alam Bhuiyan
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  3. Lianyong Qi
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  4. Hongli Nie
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  5. Wajid Rafique
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  6. Wanchun Dou
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Corresponding author

Correspondence to Wanchun Dou .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Jinjun Chen

  3. St. Francis Xavier University, Antigonish, NS, Canada

    Laurence T. Yang

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Zhao, X., Bhuiyan, M.Z.A., Qi, L., Nie, H., Rafique, W., Dou, W. (2018). TrCMP: An App Usage Inference Method for Mobile Service Enhancement. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_19

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  • DOI: https://doi.org/10.1007/978-3-030-05345-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05344-4

  • Online ISBN: 978-3-030-05345-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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