利用用户对攻击请求的抵抗实现更高效的Sybil检测机制
More Efficient Sybil Detection Mechanisms Leveraging Resistance of Users to Attack Requests
AI总结：
本文从图算法角度研究社交网络Sybil检测问题，引入用户对攻击请求的抵抗概念并提出合成图数据生成框架，设计了最大化发现良性用户与攻击边数量的高效优化算法，实验证明其作为预处理步骤能显著提升检测性能。
摘要：
我们从图算法的角度研究了社交网络中的sybil（虚假账户）检测问题，其中利用图结构信息将用户分类为sybil和良性。我们引入了用户对攻击请求（来自sybil账户的好友请求）的抵抗这一新颖概念。基于此概念，我们提出了一个支持各种攻击策略的合成图数据生成框架。接着，我们研究了一个优化问题：允许揭示部分用户的抵抗情况，旨在最大化被发现为良性的用户数量以及潜在攻击边（从sybil到良性用户的连接）的数量。此外，我们为该问题设计了高效算法，并探讨了其理论保证。最后，通过大量实验，我们证明所提出的算法在作为不同sybil检测算法的预处理步骤时，能显著提升检测性能。本工作使用的代码和数据已在GitHub公开：https://github.com/aSafarpoor/AAMAS2025-Paper/tree/main
英文摘要：
We investigate the problem of sybil (fake account) detection in social networks from a graph algorithms perspective, where graph structural information is used to classify users as sybil and benign. We introduce the novel notion of user resistance to attack requests (friendship requests from sybil accounts). Building on this notion, we propose a synthetic graph data generation framework that supports various attack strategies. We then study the optimization problem where we are allowed to reveal the resistance of a subset of users with the aim to maximize the number of users which are discovered to be benign and the number of potential attack edges (connections from a sybil to a benign user). Furthermore, we devise efficient algorithms for this problem and investigate their theoretical guarantees. Finally, through a large set of experiments, we demonstrate that our proposed algorithms improve detection performance notably when applied as a preprocessing step for different sybil detection algorithms. The code and data used in this work are publicly available on GitHub https://github.com/aSafarpoor/AAMAS2025-Paper/tree/main
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
