Aiding the Detection of Fake Accounts in Large Scale Social Online Services
Aiding the Detection of Fake Accounts in Large Scale Social Online Services
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发表时间：
2012-04
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通讯作者：
Q. Cao;Michael Sirivianos;Xiaowei Yang;Tiago Pregueiro
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作者：
Q. Cao;Michael Sirivianos;Xiaowei Yang;Tiago Pregueiro
用户越来越依赖于在线社交网络（OSN）上公开的信息的可信度。此外，OSN提供商将其商业模式建立在这些信息的适销性上。然而，OSN遭受以创建假账户的形式的滥用，假账户不对应于真实的人类。假货可以引入垃圾邮件，操纵在线评级，或利用从网络中提取的知识。OSN运营商目前花费大量资源来检测、手动验证和关闭虚假账户。Tuenti是西班牙最大的OSN，仅这项任务就有14名全职员工，产生了巨大的资金成本。由于难以可靠地捕获假的和真实的OSN简档的不同行为，因此这样的任务尚未成功地自动化。我们在OSN运营商手中引入了一个新工具，我们称之为SybilRank。它依赖于社交图属性，根据用户感知的虚假可能性对用户进行排名（Sybils）。SybilRank计算效率高，可以扩展到具有数亿个节点的图形，正如我们的Hadoop原型所证明的那样。我们在图恩蒂的行动中心部署了SybilRank我们发现，SybilRank指定的20万个账户中有90%最有可能是假的，实际上应该被暂停。另一方面，在Tuenti目前基于用户报告的方法下，只有10.5%的被检查账户确实是假的。
Users increasingly rely on the trustworthiness of the information exposed on Online Social Networks (OSNs). In addition, OSN providers base their business models on the marketability of this information. However, OSNs suffer from abuse in the form of the creation of fake accounts, which do not correspond to real humans. Fakes can introduce spam, manipulate online rating, or exploit knowledge extracted from the network. OSN operators currently expend significant resources to detect, manually verify, and shut down fake accounts. Tuenti, the largest OSN in Spain, dedicates 14 full-time employees in that task alone, incurring a significant monetary cost. Such a task has yet to be successfully automated because of the difficulty in reliably capturing the diverse behavior of fake and real OSN profiles. We introduce a new tool in the hands of OSN operators, which we call SybilRank. It relies on social graph properties to rank users according to their perceived likelihood of being fake (Sybils). SybilRank is computationally efficient and can scale to graphs with hundreds of millions of nodes, as demonstrated by our Hadoop prototype. We deployed SybilRank in Tuenti's operation center. We found that ∼90% of the 200K accounts that SybilRank designated as most likely to be fake, actually warranted suspension. On the other hand, with Tuenti's current user-report-based approach only ∼5% of the inspected accounts are indeed fake.