最近,深度学习方法已经改善了高维数据集的异常检测;然而,现有的方法没有明确地学习变量之间现有关系的结构,或者使用它们来预测时间序列的预期行为。我们的方法将结构学习方法与图神经网络相结合,另外使用注意力权重为检测到的异常提供可解释性。在两个真实传感器数据集上进行的实验表明,我们的方法比baseline方法能够更准确...
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges[J]. IEEE Access, 2022. 团队主要是韩国的IEEE Access, h-index:56, CiteScore:6.70 Abstract 图异常:是指图中不符合正常模式的图形属性或结构的模式。 解决方法:基于GNN的方法利用关于图形属性(或特征)和/或结构的信息来学习...
Recently, graph neural networks (GNNs), as a powerful deep-learning-based graph representation technique, has demonstrated superiority in leveraging the graph structure and been used in anomaly detection. In this chapter, we provide a general, comprehensive, and structured overview of the existing ...
GraphBEAN is a graph convolutional network designed by Grab for anomaly detection. This powerful model is able to handle heterogeneous data, thereby capturing data of different types from the nodes and edges. This article went through the six steps to building a link prediction GCN mo...
[1] A. Deng and B. Hooi, “Graph neural network-based anomaly detection in multivariate time series,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. See Also How to Get Best Site Performance Select the China site (in Chinese or English) for best ...
内容提示: Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesAilin Deng, Bryan HooiNational University of Singaporeailin@comp.nus.edu.sg, bhooi@comp.nus.edu.sgAbstractGiven high-dimensional time series data (e.g., sensor data),how can we detect anomalous events, such as ...
To ensure the stable long-time operation of satellites, evaluate the satellite status, and improve satellite maintenance efficiency, we propose an anomaly detection method based on graph neural network and dynamic threshold (GNN-DTAN). Firstly, we build the graph neural network model for telemetry ...
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges Hwan Kim, Byung Suk Lee, Won-Yong Shin, Senior Member, IEEE, and Sungsu Lim, Member, IEEE Weakly Supervised Anomaly Detection: A Survey Minqi Jiang,Chaochuan Hou,Ao Zheng,Xiyang Hu,Songqiao Han,Hailiang Huang,...
5.3 Network Anomaly Detection 3、Graph Embedding Algorithm In this section, we introduce the first-order graph and second-order graph of network traffic, then propose the graph embedding algorithm for these two graphs. At last, we also adopt two optimization methods to reduce the complexity of th...
回顾了异常检测(Anomaly Detection)、多元时间序列数据模型 (models for multivariate time series data)、图神经网络(Graph neural network)的研究相关工作,并指出其不足。 2.1 异常检测(Anomaly Detection) 目的是检测出偏离大部分数据的异常样本,经典方法包括基于密度的研究方法、基于线性模型的研究方法、基于距离的研究...