[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Classification of Hidden Users’ Profiles in Wireless Communications

  • Conference paper
Mobile Networks and Management (MONAMI 2011)

Abstract

The Internet can be seen as a mix of several services and applications running on top of common protocols. The emergence of several web-applications changed the users’ interaction paradigm by placing them in a more active role allowing them to share photos, videos and much more. The analysis of the profile of each user, both in wired and wireless networks, becomes very interesting for tasks such as network resources optimization, service personalization and security. In this paper, we propose a promiscuous wireless passive monitoring classification approach that can accurately create users’ profiles in terms of the used web-applications and does not require authentication with the wireless Access Point. By extracting appropriate layer 2 traffic metrics, performing a Wavelet Decomposition and analyzing the obtained scalograms, it is possible to analyze the traffic’s time and frequency components. An appropriate communication profile can then be defined in order to describe this frequency spectrum which is characteristic to each web-based application. Consequently, it is possible to identify the applications that are being used by the different connected clients and build user-profiles. Wireless traffic generated by several connected clients running some of the most significant web-based applications was captured and analyzed and the obtained results show that it is possible to obtain an accurate application traffic mapping and an accurate user profiling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Cisco ios intrusion prevention system (ips) - products and services (March 2011), http://www.cisco.com/en/US/products/ps6634/index.html

  2. Snort: Home page (March 2011), http://www.snort.org/

  3. Godoy, D., Amandi, A.: User profiling in personal information agents: a survey. Knowledge Engineering Review 20(4), 329–361 (2005)

    Article  MATH  Google Scholar 

  4. Hu, Y., Chiu, D.M., Lui, J.: Application identification based on network behavioral profiles. In: 16th International Workshop on Quality of Service, IWQoS 2008, pp. 219–228 (2008)

    Google Scholar 

  5. Huang, N.F., Jai, G.Y., Chao, H.C.: Early identifying application traffic with application characteristics. In: IEEE International Conference on Communications, ICC 2008, pp. 5788–5792 (May 2008)

    Google Scholar 

  6. Iglesias, J.A., Angelov, P., Ledezma, A., Sanchis, A.: Creating evolving user behavior profiles automatically. IEEE Transactions on Knowledge and Data Engineering 99 (2011) (preprints)

    Google Scholar 

  7. Madhukar, A., Williamson, C.: A longitudinal study of p2p traffic classification. In: 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2006, pp. 179–188 (September 2006)

    Google Scholar 

  8. Moore, A.W., Papagiannaki, K.: Toward the Accurate Identification of Network Applications. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 41–54. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Moore, A.W., Zuev, D.: Internet traffic classification using bayesian analysis techniques. In: ACM SIGMETRICS, pp. 50–60 (2005)

    Google Scholar 

  10. Nguyen, T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys Tutorials 10(4), 56–76 (2008)

    Article  Google Scholar 

  11. Rocha, E., Salvador, P., Nogueira, A.: Detection of Illicit Network Activities based on Multivariate Gaussian Fitting of Multi-Scale Traffic Characteristics. In: IEEE International Conference on Communications, ICC 2011 (June 2011)

    Google Scholar 

  12. Slavic, J., Simonovski, I., Boltezar, M.: Damping identification using a continuous wavelet transform: application to real data. Journal of Sound and Vibration 262(2), 291–307 (2003)

    Article  Google Scholar 

  13. Tavallaee, M., Lu, W., Ghorbani, A.A.: Online classification of network flows. In: Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference, pp. 78–85. IEEE Computer Society, Washington, DC (2009)

    Chapter  Google Scholar 

  14. Trestian, I., Ranjan, S., Kuzmanovic, A., Nucci, A.: Googling the internet: Profiling internet endpoints via the world wide web. IEEE/ACM Transactions on Networking 18(2), 666–679 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Rocha, E., Salvador, P., Nogueira, A. (2012). Classification of Hidden Users’ Profiles in Wireless Communications. In: Pentikousis, K., Aguiar, R., Sargento, S., Agüero, R. (eds) Mobile Networks and Management. MONAMI 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30422-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-30422-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30421-7

  • Online ISBN: 978-3-642-30422-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics