Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

中文翻译：

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将基于图的 Sybil 检测方法解释为低通滤波


在线社交网络 (OSN) 受到 Sybil 攻击的威胁，这种攻击会在 OSN 上创建虚假帐户（也称为 Sybil），并将其用于各种恶意活动。因此，Sybil检测是OSN安全的一项基本任务。大多数现有的 Sybil 检测方法都是基于 OSN 的图结构，并且最近提出了各种方法。然而，尽管几乎所有方法都在检测性能和噪声鲁棒性方面进行了实验比较，但对它们的理论理解仍然缺乏。在这项研究中，我们表明现有的基于图的 Sybil 检测方法可以在统一的低通滤波框架中解释。该框架使我们能够从滤波器核属性和移位矩阵的谱两个角度对每种方法进行理论上的比较和分析。我们的分析表明，每种方法的检测性能取决于低通滤波的有效性。此外，在分析的基础上，我们提出了一种新的 Sybil 检测方法，称为 SybilHeat。对合成图和真实社交网络的数值实验表明，SybilHeat 在具有各种结构属性的图上始终表现良好。本研究为基于图的 Sybil 检测奠定了理论基础，并有助于更好地理解 Sybil 检测方法。