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Abstract-Social networking is one of the most popular Internet activities, with millions of users from around the world. The time spent on sites like Facebook or LinkedIn is constantly increasing at an impressive rate. At the same time, users populate their online profile with a plethora of information that aims at providing a complete and accurate representation of themselves. Attackers may duplicate a user's online presence in the same or across different social networks and, therefore, fool other users into forming trusting social relations with the fake profile. By abusing that implicit trust transferred from the concept of relations in the physical world, they can launch phishing attacks, harvest sensitive user information, or cause unfavorable repercussions to the legitimate profile's owner.In this paper we propose a methodology for detecting social network profile cloning. We present the architectural design and implementation details of a prototype system that can be employed by users to investigate whether they have fallen victims to such an attack. Our experimental results from the use of this prototype system prove its efficiency and also demonstrate its simplicity in terms of deployment by everyday users. Finally, we present the findings from a short study in terms of profile information exposed by social network users.

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Social networking is one of the most popular Internet activities with millions of members from around the world. However, users are unaware of the privacy risks involved. Even if they protect their private information, their name is enough to be used for malicious purposes. In this paper we demonstrate and evaluate how names extracted from social networks can be used to harvest email addresses as a first step for personalized phishing campaigns. Our blind harvesting technique uses names collected from the Facebook and Twitter networks as query terms for the Google search engine, and was able to harvest almost 9 million unique email addresses. We compare our technique with other harvesting methodologies, such as crawling the World Wide Web and dictionary attacks, and show that our approach is more scalable and efficient than the other techniques. We also present three targeted harvesting techniques that aim to collect email addresses coupled with personal information for the creation of personalized phishing emails. By using information available in Twitter to narrow down the search space and, by utilizing the Facebook email search functionality, we are able to successfully map 43.4% of the user profiles to their actual email address. Furthermore, we harvest profiles from Google Buzz, 40% of whom provide a direct mapping to valid Gmail addresses.