A Deep Graph Convolution Network-Based Abnormity Detection Model for Largescale Behavioral Data

• Published in: IEEE access Vol. 12; pp. 94380 - 94392
• Main Authors: Liang, Shaolin, Shao, Kangjie
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Anomaly detection; Behavior; behavioral modeling; Behavioral sciences; Complex networks; Convolution; Convolutional neural networks; Data models; Deep learning; Feature extraction; Graph convolution network; Graph convolutional networks; Graphical representations; high-dimensional data; Internet; Performance evaluation; Social networks

It remains important to make abnormity detection from largescale behavioral data of Internet. Existing related approaches mostly failed to employ high-dimensional characteristics of Internet data, which limits the detection effect. To deal with this issue, we introduce graph convolution network (GCN...