计算机工程2024，Vol.50Issue(3)：156-165,10.DOI:10.19678/j.issn.1000-3428.0067948
基于角度的图神经网络高维数据异常检测方法
Angle-based Graph Neural Network Method for Anomaly Detection in High Dimensional Data
摘要
Abstract
In high-dimensional data spaces,most data are located at the edges of the high-dimensional space and distributed sparsely,resulting in the problem of"curse of dimensionality",which makes existing anomaly detection methods unable to ensure the accuracy of anomaly detection.To address this problem,an Angle-based Graph Neural Network(A-GNN)high-dimensional data anomaly detection method is proposed.First,the data used for training are expanded by uniformly sampling the data space and perturbing the initial training data.Second,the k-nearest neighbor relationship is used to construct a k-nearest neighbor relationship graph of the training data,and the variance of the k-nearest neighbor element distance weighted angle is used as the initial anomaly factor for the nodes in the k-nearest neighbor relationship graph.Finally,by training a GNN model,information exchange between nodes is achieved,enabling adjacent nodes to learn from each other and effectively evaluate anomalies.The A-GNN method is experimentally compared with nine typical anomaly detection methods on six natural datasets.The results demonstrate that A-GNN achieved the highest Area Under the Curve(AUC)value in five datasets,which can significantly improve the anomaly detection accuracy of various dimensions of data.On some true high-dimensional data,the AUC of anomaly detection increased by more than 40%.Compared with three k-nearest neighbor-based anomaly detection methods at different k values,A-GNN can effectively avoid the impact of k values on detection results by utilizing information exchange between GNN nodes,and the method has stronger robustness.关键词
异常检测/基于角度的异常评估/图神经网络/高维数据/k近邻Key words
anomaly detection/angle-based anomaly assessment/Graph Neural Network(GNN)/high-dimensional data/k-nearest neighbor分类
信息技术与安全科学引用本文复制引用
王俊,赖会霞,万玥,张仕..基于角度的图神经网络高维数据异常检测方法[J].计算机工程,2024,50(3):156-165,10.基金项目
国家自然科学基金(61772004) （61772004）
福建省自然科学基金(2020J01161) （2020J01161）
福建省科技厅对外合作项目(2023I0013). （2023I0013）
