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This paper proposes a new method named evolving-graph generation framework to simultaneously solve the complexity and dynamic nature of the attribute networks that can occur in graph-based anomaly detection with Graph Neural Networks (GNN). The proposed framework consists of two components. The first component is a feature selection method that hybridizes filter-based and wrapper-based techniques to reduce the snapshots. The second component is an association method based on temporal patternsdoi:10.22667/jisis.2022.02.28.072 dblp:journals/jisis/KimKJS22 fatcat:uncpemjenbgavni65vrnhzszzy