论文标题
从双层到一级：结构性攻击的框架到绘制异常检测
From Bi-Level to One-Level: A Framework for Structural Attacks to Graph Anomaly Detection
论文作者
论文摘要
图神经网络的成功刺激了图挖掘的繁荣以及包括图异常检测（GAD）在内的相应下游任务。但是，已经探讨了这些图形挖掘方法容易受到关系数据的结构操作。也就是说，攻击者可能会恶意扰动图形结构，以帮助目标节点逃避异常检测。在本文中，我们探讨了两个典型GAD系统的结构脆弱性：基于FEXTRA的无监督和基于GCN的GAD。具体而言，针对GAD的结构中毒攻击被表达为复杂的双层优化问题。然后，我们的第一个主要贡献是将双层问题转变为一级利用不同的回归方法。此外，我们提出了一种利用梯度信息来优化离散域中单级优化问题的新方法。全面的实验证明了我们提出的攻击算法二进制攻击的有效性。
The success of graph neural networks stimulates the prosperity of graph mining and the corresponding downstream tasks including graph anomaly detection (GAD). However, it has been explored that those graph mining methods are vulnerable to structural manipulations on relational data. That is, the attacker can maliciously perturb the graph structures to assist the target nodes to evade anomaly detection. In this paper, we explore the structural vulnerability of two typical GAD systems: unsupervised FeXtra-based GAD and supervised GCN-based GAD. Specifically, structural poisoning attacks against GAD are formulated as complex bi-level optimization problems. Our first major contribution is then to transform the bi-level problem into one-level leveraging different regression methods. Furthermore, we propose a new way of utilizing gradient information to optimize the one-level optimization problem in the discrete domain. Comprehensive experiments demonstrate the effectiveness of our proposed attack algorithm BinarizedAttack.
