Reverse Social Engineering Attacks in Online Social Networks

  • Danesh Irani
  • Marco Balduzzi
  • Davide Balzarotti
  • Engin Kirda
  • Calton Pu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6739)


Social networks are some of the largest and fastest growing online services today. Facebook, for example, has been ranked as the second most visited site on the Internet, and has been reporting growth rates as high as 3% per week. One of the key features of social networks is the support they provide for finding new friends. For example, social network sites may try to automatically identify which users know each other in order to propose friendship recommendations.

Clearly, most social network sites are critical with respect to user’s security and privacy due to the large amount of information available on them, as well as their very large user base. Previous research has shown that users of online social networks tend to exhibit a higher degree of trust in friend requests and messages sent by other users. Even though the problem of unsolicited messages in social networks (i.e., spam) has already been studied in detail, to date, reverse social engineering attacks in social networks have not received any attention. In a reverse social engineering attack, the attacker does not initiate contact with the victim. Rather, the victim is tricked into contacting the attacker herself. As a result, a high degree of trust is established between the victim and the attacker as the victim is the entity that established the relationship.

In this paper, we present the first user study on reverse social engineering attacks in social networks. That is, we discuss and show how attackers, in practice, can abuse some of the friend-finding features that online social networks provide with the aim of launching reverse social engineering attacks. Our results demonstrate that reverse social engineering attacks are feasible and effective in practice.


social engineering social networks privacy 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Balduzzi, M., Platzer, C., Holz, T., Kirda, E., Balzarotti, D., Kruegel, C.: Abusing Social Networks for Automated User Profiling. In: Jha, S., Sommer, R., Kreibich, C. (eds.) RAID 2010. LNCS, vol. 6307, pp. 422–441. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Bilge, L., Strufe, T., Balzarotti, D., Kirda, E.: All Your Contacts Are Belong to Us: Automated Identity Theft Attacks on Social Networks. In: 18th International Conference on World Wide Web, WWW (2009)Google Scholar
  6. 6.
    Douceur, J.R.: The sybil attack. In: Druschel, P., Kaashoek, M.F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, p. 251. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Flaxman, A.: Expansion and lack thereof in randomly perturbed graphs. Internet Mathematics 4(2), 131–147 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Irani, D., Webb, S., Giffin, J., Pu, C.: Evolutionary study of phishing. In: eCrime Researchers Summit, pp. 1–10. IEEE, Los Alamitos (2008)Google Scholar
  9. 9.
    Irani, D., Webb, S., Pu, C., Li, K.: Study of Trend-Stuffing on Twitter through Text Classification. In: Collaboration, Electronic messaging, Anti-Abuse and Spam Conference, CEAS (2010)Google Scholar
  10. 10.
    Jagatic, T.N., Johnson, N.A., Jakobsson, M., Menczer, F.: Social phishing. Commun. ACM 50(10), 94–100 (2007)CrossRefGoogle Scholar
  11. 11.
    Jakobsson, M., Finn, P., Johnson, N.: Why and How to Perform Fraud Experiments. IEEE Security & Privacy 6(2), 66–68 (2008)CrossRefGoogle Scholar
  12. 12.
    Jakobsson, M., Ratkiewicz, J.: Designing ethical phishing experiments: a study of (ROT13) rOnl query features. In: 15th International Conference on World Wide Web, WWW (2006)Google Scholar
  13. 13.
    Lauinger, T., Pankakoski, V., Balzarotti, D., Kirda, E.: Honeybot, your man in the middle for automated social engineering. In: LEET 2010, 3rd USENIX Workshop on Large-Scale Exploits and Emergent Threats, San Jose (2010)Google Scholar
  14. 14.
    Mitnick, K., Simon, W.L., Wozniak, S.: The Art of Deception: Controlling the Human Element of Security. Wiley, Chichester (2002)Google Scholar
  15. 15.
    Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  16. 16.
    Stringhini, G., Kruegel, C., Vigna, G.: Detecting Spammers on Social Networks. In: Annual Computer Security Applications Conference, ACSAC (2010)Google Scholar
  17. 17.
    Webb, S., Caverlee, J., Pu, C.: Social Honeypots: Making Friends with a Spammer Near You. In: Conference on Email and Anti-Spam, CEAS (2008)Google Scholar
  18. 18.
    Yu, H., Kaminsky, M., Gibbons, P., Flaxman, A.: Sybilguard: defending against sybil attacks via social networks. In: Proceedings of the 2006 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 267–278. ACM, New York (2006)Google Scholar
  19. 19.
    Yu, H., Kaminsky, M., Gibbons, P. B., Flaxman, A.: SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks. In: IEEE Symposium on Security and Privacy (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Danesh Irani
    • 1
  • Marco Balduzzi
    • 2
  • Davide Balzarotti
    • 2
  • Engin Kirda
    • 3
  • Calton Pu
    • 1
  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Institute EurecomSophia AntipolisFrance
  3. 3.Northeastern UniversityBostonUSA

Personalised recommendations