Reverse Social Engineering Attacks in Online Social Networks
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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.
Keywordssocial engineering social networks privacy
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- 1.Sophos Facebook ID Probe (2008), http://www.sophos.com/pressoffice/news/articles/2007/08/facebook.html
- 2.Facebook Statistics (2010), http://www.facebook.com/press/info.php?statistics
- 3.Sophos Security Threat 2010 (2010), http://www.sophos.com/sophos/docs/eng/papers/sophos-security-threat-report-jan-2010-wpna.pdf
- 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
- 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.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
- 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.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.Mitnick, K., Simon, W.L., Wozniak, S.: The Art of Deception: Controlling the Human Element of Security. Wiley, Chichester (2002)Google Scholar
- 16.Stringhini, G., Kruegel, C., Vigna, G.: Detecting Spammers on Social Networks. In: Annual Computer Security Applications Conference, ACSAC (2010)Google Scholar
- 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.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.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