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All your contacts are belong to us: automated identity theft attacks on social networks
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International World Wide Web Conference archive
Proceedings of the 18th international conference on World wide web table of contents
Madrid, Spain
SESSION: Security and privacy/session: web security table of contents
Pages 551-560  
Year of Publication: 2009
ISBN:978-1-60558-487-4
Authors
Leyla Bilge  Eurecom, Sophia Antipolis, France
Thorsten Strufe  Eurecom, Sophia Antipolis, France
Davide Balzarotti  Eurecom, Sophia Antipolis, France
Engin Kirda  Eurecom, Sophia Antipolis, France
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Social networking sites have been increasingly gaining popularity. Well-known sites such as Facebook have been reporting growth rates as high as 3% per week. Many social networking sites have millions of registered users who use these sites to share photographs, contact long-lost friends, establish new business contacts and to keep in touch. In this paper, we investigate how easy it would be for a potential attacker to launch automated crawling and identity theft attacks against a number of popular social networking sites in order to gain access to a large volume of personal user information. The first attack we present is the automated identity theft of existing user profiles and sending of friend requests to the contacts of the cloned victim. The hope, from the attacker's point of view, is that the contacted users simply trust and accept the friend request. By establishing a friendship relationship with the contacts of a victim, the attacker is able to access the sensitive personal information provided by them. In the second, more advanced attack we present, we show that it is effective and feasible to launch an automated, cross-site profile cloning attack. In this attack, we are able to automatically create a forged profile in a network where the victim is not registered yet and contact the victim's friends who are registered on both networks. Our experimental results with real users show that the automated attacks we present are effective and feasible in practice.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Leyla Bilge: colleagues
Thorsten Strufe: colleagues
Davide Balzarotti: colleagues
Engin Kirda: colleagues