![]() ![]() Society for Economic Dynamics Meeting Papers (2005) ![]() Hitsch, G.J., Hortacsu, A., Ariely, D.: What makes you click: an empirical analysis of online dating. Herley, C.: Why do Nigerian scammers say they are from Nigeria? In: Workshop on the Economics of Information Security (WEIS) (2012) Hao, S., Syed, N.A., Feamster, N., Gray, A.G., Krasser, S.: Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine. In: ACM Conference on Computer and Communications Security (CCS) (2010) Grier, C., Thomas, K., Paxson, V., Zhang, M.: the underground on 140 characters or less. Glickman, H.: The nigerian "419" advance fee scams: prank or peril? Can. Gao, Y., Zhao, G.: Knowledge-based information extraction: a case study of recognizing emails of nigerian frauds. In: Internet Measurement Conference (IMC) (2010) Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.: Detecting and characterizing social spam campaigns. In: Symposium on Network and Distributed System Security (NDSS) (2012) Gao, H., Chen, Y., Lee, K., Palsetia, D., Choudhary, A.: Towards Online spam filtering in social networks. thesis, Massachusetts Institute of Technology (2004) In: Symposium on Network and Distributed System Security (NDSS) (2013)įiore, A., Tresolini, R.: Romantic regressions: an analysis of behavior in online dating systems. In: Association for Business Communication Annual Convention (2005)Įgele, M., Stringhini, G., Kruegel, C., Vigna, G.: Compa: detecting compromised accounts on social networks. In: Proceedings of the 5th International Conference on Communities and Technologies (2011)ĭrucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. In: Symposium on Network and Distributed System Security (NDSS) (2012)Ĭhen, L., Nayak, R.: Social network analysis of an online dating network. In: Symposium on Network and Distributed System Security (NDSS) (2012)Ĭao, Y., Yegneswaran, V., Possas, P., Chen, Y.: PathCutter: severing the self-propagation path of XSS javascript worms in social web networks. In: Conference on Email and Anti-spam (CEAS) (2010)Ĭai, Z., Jermaine, C.: The latent community model for detecting sybils in social networks. Trend Micro Threat Research (2009)īenevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In IEEE Symposium on Security and Privacy (2012)īaltazar, J., Costoya, J., Flores, R.: KOOBFACE: the largest web 2.0 botnet explained. This process is experimental and the keywords may be updated as the learning algorithm improves.Īfroz, S., Brennan, M., Greenstadt, R.: Detecting hoaxes, frauds, and deception in writing style online. These keywords were added by machine and not by the authors. Our results shed light on the threats associated to online dating scams, and can help researchers and practitioners in developing effective countermeasures to fight them. We show that different types of scammers target a different demographics on the site, and therefore set up accounts with different characteristics. We analyze the scam accounts detected on a popular online dating site over a period of eleven months, and provide a taxonomy of the different types of scammers that are active in the online dating landscape. In this paper we perform the first large-scale study of online dating scams. Such scams span from schemes similar to traditional advertisement of illicit services or goods (i.e., spam) to advanced schemes, in which the victim starts a long-distance relationship with the scammer and is eventually extorted money. Online dating sites provide a valuable platform not only for single people trying to meet a life partner, but also for cybercriminals, who see in people looking for love easy victims for scams. Online dating sites are experiencing a rise in popularity, with one in five relationships in the United States starting on one of these sites.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |