论文标题
基于双曲线自我监管的基于学习的网络异常检测
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection
论文作者
论文摘要
归因网络上的异常检测最近在许多研究领域（例如控制论异常检测和财务欺诈检测）受到了越来越多的关注。随着深度学习在图表表示上的广泛应用，现有方法选择将欧几里得图编码器作为骨干，这可能会失去重要的层次结构信息，尤其是在复杂的网络中。为了解决这个问题，我们提出了使用双曲线自我监督的对比学习有效的异常检测框架。具体而言，我们首先通过执行子图抽样进行数据增强。然后，我们通过指数映射和对数映射利用双曲线空间中的分层信息，并通过通过区分过程从负对中减去正对的分数来获得异常得分。最后，在四个现实世界数据集上进行的广泛实验表明，我们的方法比代表性的基线方法出色。
Anomaly detection on the attributed network has recently received increasing attention in many research fields, such as cybernetic anomaly detection and financial fraud detection. With the wide application of deep learning on graph representations, existing approaches choose to apply euclidean graph encoders as their backbone, which may lose important hierarchical information, especially in complex networks. To tackle this problem, we propose an efficient anomaly detection framework using hyperbolic self-supervised contrastive learning. Specifically, we first conduct the data augmentation by performing subgraph sampling. Then we utilize the hierarchical information in hyperbolic space through exponential mapping and logarithmic mapping and obtain the anomaly score by subtracting scores of the positive pairs from the negative pairs via a discriminating process. Finally, extensive experiments on four real-world datasets demonstrate that our approach performs superior over representative baseline approaches.
