SybilBlind：无需手动标签即可检测在线社交网络中的虚假用户
SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels
摘要：
检测在线社交网络中的假冒用户（也称为Sybils）是一个基本的安全研究问题。最先进的方法依赖于大量手动标记的用户作为训练集。这些方法受到三个关键限制：1）手动标记大型训练集是耗时且昂贵的，2）它们不能及时检测新的Sybil，以及3）它们容易受到利用训练集信息的Sybil攻击。在这项工作中，我们提出了SybilBlind，一个基于结构的Sybil检测框架，不依赖于手动标记的训练集。SybilBlind在与最先进的基于结构的方法相同的威胁模型下工作。我们使用1）合成Sybils的社交网络和2）真实Sybils的两个Twitter数据集来证明SybilBlind的有效性。例如，SybilBlind在Twitter数据集上实现了0.98的AUC。
英文摘要：
Detecting fake users (also called Sybils) in online social networks is a basic security research problem. State-of-the-art approaches rely on a large amount of manually labeled users as a training set. These approaches suffer from three key limitations: 1) it is time-consuming and costly to manually label a large training set, 2) they cannot detect new Sybils in a timely fashion, and 3) they are vulnerable to Sybil attacks that leverage information of the training set. In this work, we propose SybilBlind, a structure-based Sybil detection framework that does not rely on a manually labeled training set. SybilBlind works under the same threat model as state-of-the-art structure-based methods. We demonstrate the effectiveness of SybilBlind using 1) a social network with synthetic Sybils and 2) two Twitter datasets with real Sybils. For instance, SybilBlind achieves an AUC of 0.98 on a Twitter dataset.
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