On a Machine Learning Approach for the Detection of Impersonation Attacks in Social Networks

  • Esther Villar-RodriguezEmail author
  • Javier Del Ser
  • Sancho Salcedo-Sanz
Part of the Studies in Computational Intelligence book series (SCI, volume 570)


Lately the proliferation of social networks has given rise to a myriad of fraudulent strategies aimed at getting some sort of benefit from the attacked individual. Despite most of them being exclusively driven by economic interests, the so called impersonation, masquerading attack or identity fraud hinges on stealing the credentials of the victim and assuming his/her identity to get access to resources (e.g. relationships or confidential information), credit and other benefits in that person’s name. While this problem is getting particularly frequent within the teenage community, the reality is that very scarce technological approaches have been proposed in the literature to address this issue which, if not detected in time, may catastrophically unchain other fatal consequences to the impersonated person such as bullying and intimidation. In this context, this paper delves into a machine learning approach that permits to efficiently detect this kind of attacks by solely relying on connection time information of the potential victim. The manuscript will demonstrate how these learning algorithms - in particular, support vector classifiers - can be of great help to understand and detect impersonation attacks without compromising the user privacy of social networks.


Impersonation Social Networks Support Vector Machines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pew Internet research on social networking, (retrieved on April 2014)
  2. 2.
    Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A., Joshi, A.: Social Ties and Their Relevance to Churn in Mobile Telecom Networks. In: Proceedings of the 11th International Conference on Extending Database Technology, pp. 668–677 (2008)Google Scholar
  3. 3.
    Eysenbach, G.: Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness. Journal of Medical Internet Research 10(3) (2008)Google Scholar
  4. 4.
    Zeng, L., Hall, H., Pitts, M.J.: Cultivating a Community of Learners. The Potential Challenges of Social Media in Higher Education. In: Noor Al-Deen, H., Hendricks, J.A. (eds.) Social Media: Usage and Impact. Lexington Books (2011)Google Scholar
  5. 5.
    Twitter: Keeping our users secure, (retrieved on April 2014)
  6. 6.
    Martin, A., Anutthamaa, N.B., Sathyavathy, M., Saint Francois, M.M., Venkatesan, P.: A Framework for Predicting Phishing Websites Using Neural Networks. IJCSI International Journal of Computer Science Issues 8(2), 330–336 (2011)Google Scholar
  7. 7.
    Salem, M.B., Stolfo, S.J.: Modeling User Search Behavior for Masquerade Detection. In: Sommer, R., Balzarotti, D., Maier, G. (eds.) RAID 2011. LNCS, vol. 6961, pp. 181–200. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Egele, M., Stringhini, G., Kruegel, C., Vigna, G.: COMPA: Detecting Compromised Accounts on Social Networks. In: ISOC Network and Distributed System Security Symposium, NDSS (2013)Google Scholar
  9. 9.
    Gao, H., Chen, Y., Lee, K., Palsetia, D., Choudhary, A.: Towards Online Spam Filtering in Social Networks. In: ISOC Network and Distributed System Security Symposium, NDSS (2012)Google Scholar
  10. 10.
    Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  11. 11.
    Miyamoto, D., Hazeyama, H., Kadobayashi, Y.: A Proposal of the AdaBoost-based Detection of Phishing Sites. In: Proceedings of the Joint Workshop on Information Security (2007)Google Scholar
  12. 12.
    Zhang, Y., Hong, J., Cranor, L.: Cantina: A Content-based Approach to Detecting Phishing Web Sites. In: Proceedings of the International World Wide Web Conference, WWW (2007)Google Scholar
  13. 13.
    Liu, W., Huang, G., Liu, X., Zhang, M., Deng, X.: Detection of Phishing Web Pages based on Visual Similarity. In: Proceedings of the International World Wide Web Conference (WWW), pp. 1060–1061 (2005)Google Scholar
  14. 14.
    Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications, 2nd edn., pp. 207–212. Springer (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Esther Villar-Rodriguez
    • 1
    Email author
  • Javier Del Ser
    • 1
  • Sancho Salcedo-Sanz
    • 2
  1. 1.TECNALIA. OPTIMA UnitDerioSpain
  2. 2.Universidad de AlcaláMadridSpain

Personalised recommendations