Detecting Spammers on Social Networks

Detecting Spammers on Social Networks
Gianluca Stringhini
PhD student at UC Santa Barbara

DEI - Seminar Room
July 6th, 2010


Social networking has become a popular way for users to meet and interact online. Users spend a significant amount of time on popular social network platforms (such as Facebook, MySpace, or Twitter), storing and sharing a wealth of personal information.
This information, as well as the possibility of contacting thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns.
In our work, we analyzed to which extent spam has entered social networks. More precisely, we analyze how spammers who target social networking sites act. To collect the data about spamming activity, we created a large and diverse set of “honey-profiles” on threelarge social networking sites, and logged the kind of contacts and messages that they received.
We then analyzed the collected data and identified anomalous behavior of users who contacted our profiles. Finally, we developed techniques to detect spammers in social networks, and to aggregate their messages in large spam campaigns. Our analysis shows that it is possible to automatically identify the accounts used by spammers, and the result of our analysis have been used as the basis for take-down actions in large-scale social networks. During this study, we collaborated with Twitter and correctly detected and deleted 15,857 spam profiles.

Short bio:

Gianluca Stringhini is a PhD student at UC Santa Barbara, working at the Computer Security Lab. He got his B.S. and M.S. in computer engineering from the University of Genova, Italy. His research interests include many aspects of computer security, in particular social network security, spam analysis and intrusion detection.

Stefano Zanero

Research area:
Systems architectures