FakeBook: Detecting Fake Profiles in On-Line Social Networks

Abstract

On-line Social Networks (OSNs) are increasingly influencing the way people communicate with each other and share personal, professional and political information. Like the cyberspace in Internet, the OSNs are attracting the interest of the malicious entities that are trying to exploit the vulnerabilities and weaknesses of the OSNs. Increasing reports of the security and privacy threats in the OSNs is attracting security researchers trying to detect and mitigate threats to individual users. With many OSNs having tens or hundreds of million users collectively generating billions of personal data content that can be exploited, detecting and preventing attacks on individual user privacy is a major challenge. Most of the current research has focused on protecting the privacy of an existing online profile in a given OSN. Instead, we note that there is a risk of not having a profile in the last fancy social network! The risk is due to the fact that an adversary may create a fake profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with the friends of victim of identity theft, with the final target of stealing personal information of the victim, via interacting online with the friends of the victim. In this paper, we report on the investigation we did on a possible approach to mitigate this problem. In doing so, we also note that we are the first ones to analyze social network graphs from a dynamic point of view within the context of privacy threats.