论文标题
早期基于图的异常检测的动作序列增强
Action Sequence Augmentation for Early Graph-based Anomaly Detection
论文作者
论文摘要
Web平台的扩散为在线滥用创造了激励措施。提出了许多基于图的异常检测技术来识别可疑的帐户和行为。但是，一旦用户执行许多此类行为，大多数人都会检测到异常。当用户观察到的数据在早期阶段受到限制时，他们的性能会受到很大的阻碍，这需要改进以最大程度地减少财务损失。在这项工作中，我们提出了Eland，这是一个新型框架，它使用动作序列增强进行早期异常检测。 Eland利用序列预测器来预测每个用户的下一个操作，并利用动作序列增强和用户表演图异常检测之间的相互增强。三个现实世界数据集的实验表明，Eland提高了各种基于图的异常检测方法的性能。使用Eland，在早期阶段的异常检测性能要比非凸出的方法更好，这些方法需要明显地观察到的数据在ROC曲线下的区域高达15％。
The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have performed many such behaviors. Their performance is substantially hindered when the users' observed data is limited at an early stage, which needs to be improved to minimize financial loss. In this work, we propose Eland, a novel framework that uses action sequence augmentation for early anomaly detection. Eland utilizes a sequence predictor to predict next actions of every user and exploits the mutual enhancement between action sequence augmentation and user-action graph anomaly detection. Experiments on three real-world datasets show that Eland improves the performance of a variety of graph-based anomaly detection methods. With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.
