一种用于攻击社交网络对齐的低成本、高影响节点注入方法
A Low-cost, High-impact Node Injection Approach for Attacking Social Network Alignment
AI总结：
针对社交网络对齐攻击的实际可行性难题，提出DPNIA节点注入框架，通过脆弱性评估和动态规划搜索低成本链接注入策略，在四个真实数据集上显著优于基线。
摘要：
社交网络对齐（SNA）对各类下游应用具有重要意义，促使众多专业人员开发并共享SNA工具。遗憾的是，这些工具可能被恶意行为者利用，以整合敏感用户信息，从而带来网络安全风险。尽管许多研究人员已经通过网络修改攻击的方式探索攻击SNA（ASNA），但实际可行性仍然是一项挑战。本文介绍了一种新颖的方法，即节点注入攻击。为了克服在有限时间内进行建模与求解以及平衡成本和收益的问题，我们提出了一种基于动态规划的低成本、高影响节点注入攻击（DPNIA）框架。DPNIA将ASNA建模为最大化已确认错误对应节点对数量的问题，这些节点对的相似度分数高于现有节点之间的节点对，从而使ASNA可求解。同时，该框架采用跨网络评估方法识别节点脆弱性，促进从易到难的渐进式攻击。此外，它利用基于动态规划的最优注入策略搜索方法，确定应在注入节点与现有节点之间添加哪些链接，从而以低成本实现攻击效果的高影响。在四个真实世界数据集上的实验一致表明，DPNIA持续且显著优于多种攻击基线。
英文摘要：
Social network alignment (SNA) holds significant importance for various downstream applications, prompting numerous professionals to develop and share SNA tools. Unfortunately, these tools can be exploited by malicious actors to integrate sensitive user information, posing cybersecurity risks. While many researchers have explored attacking SNA (ASNA) through a network modification attack way, practical feasibility remains a challenge. This paper introduces a novel approach, the node injection attack. To overcome the problem of modeling and solving within a limited time and balancing costs and benefits, we propose a low-cost, high-impact node injection attack via dynamic programming (DPNIA) framework. DPNIA models ASNA as a problem of maximizing the number of confirmed incorrect correspondent node pairs who have a greater similarity scores than the pairs between existing nodes, making ASNA solvable. Meanwhile, it employs a cross-network evaluation method to identify node vulnerability, facilitating a progressive attack from easy to difficult. Additionally, it utilizes an optimal injection strategy searching method, based on dynamic programming, to determine which links should be added between injected nodes and existing nodes, thereby achieving a high impact for attack effectiveness at a low cost. Experiments on four real-world datasets consistently demonstrate that DPNIA consistently and significantly outperforms various attack baselines.
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。
