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Spot the Hotspot: Wi-Fi Hotspot Classification from Internet Traffic

  • Andrey Finkelshtein
  • Rami Puzis
  • Asaf Shabtai
  • Bronislav Sidik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)

Abstract

The meteoric progress of Internet technologies and PDA (personal digital assistant) devices has made public Wi-Fi hotspots very popular. Nowadays, hotspots can be found almost anywhere: organizations, home networks, public transport systems, restaurants, etc. The Internet usage patterns (e.g. browsing) differ with the hotspot venue. This insight introduces new traffic profiling opportunities. Using machine learning techniques we show that it is possible to infer types of venues that provide Wi-Fi access (e.g., organizations and hangout places) by analyzing the Internet traffic of connected mobile phones. We show that it is possible to infer the user’s current venue type disclosing his/her current context. This information can be used for improving personalized and context aware services such as web search engines or online shops, without the presence on user’s device. In this paper we evaluate venue type inference based on mobile phone traffic collected from 115 college students and analyze their Internet behavior across the different venues types.

Keywords

Smartphone Machine learning Classification Wi-Fi Hotspot 

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, US (2011)CrossRefGoogle Scholar
  2. 2.
    Adults’ Media Use and Attitudes Report. Ofcom (2014)Google Scholar
  3. 3.
    Afanasyev, M., Csirao, B.Q., Chen, T., Voelker, G., Snoeren, A.: Usage patterns in an urban WiFi network. IEEE/ACM Trans. Netw. 18(5), 1359–1372 (2010)CrossRefGoogle Scholar
  4. 4.
    Balachandran, A., Voelker, G.M., Bahl, P., Rangan, P.V.: Characterizing user behavior and network performance in a public wireless lan. In: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, pp. 195–205 (2002)Google Scholar
  5. 5.
    Manweiler, J., Santhapuri, N., Choudhury, R., Nelakuditi, S.: Predicting length of stay at WiFi hotspots. In: INFOCOM, 2013 Proceedings IEEE. Turin (2013)Google Scholar
  6. 6.
    Mark Hall, E.F.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 11 (2009)Google Scholar
  7. 7.
    Miettinen, M., Heuser, S., Kronz, W., Sadeghi, A.-R., Asokan, N.: ConXsense – context profiling and classification for context-aware access control. In: ASIACCS (2014)Google Scholar
  8. 8.
    Namiot, D.: Context-aware Browsing – a practical approach. In: 2012 6th International Conference on Next Generation Mobile Applications, Services and Technologies. Paris (2012)Google Scholar
  9. 9.
    Pentland, A.S., Aharony, N., Pan, W., Sumter, C., Gardner, A.: Funf: Open sensing framework (2013)Google Scholar
  10. 10.
    Qin, W., Zhang, J., Li, B., Zhu, H., Sun, Y.: Mo-Fi: discovering human presence activity with smartphones using Non-intrusive Wi-Fi sniffers. In: 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC) (2013)Google Scholar
  11. 11.
    The Infinite Dial 2013. Navigating Digital Platforms. Edison Reseach and Arbitron (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrey Finkelshtein
    • 1
  • Rami Puzis
    • 1
  • Asaf Shabtai
    • 1
  • Bronislav Sidik
    • 1
  1. 1.Ben Gurion University of the NegevBeershebaIsrael

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