LATIDIA · Ciberseguridad

FoundAna: un modelo de base asistido por GNN para la detección de anomalías gráficas

arXiv:2609.18107v1 Announce Type: cross Resumen: La detección de anomalías gráficas tiene como objetivo identificar estructuras gráficas (por ejemplo, nodos, bordes o subgrafos) que se desvían significativamente de los patrones esperados, lo que admite ap crítica

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arXiv:2609.18107v1 Announce Type: cross Abstract: Graph anomaly detection aims to identify graph structures (e.g., nodes, edges, or subgraphs) that deviate significantly from expected patterns, which supports critical applications in fraud detection, spam identification, network intrusion, etc. Despite the growing methods in the field, existing approaches follow a one-model-per-dataset paradigm, limiting their transferability across diverse real-world scenarios due to task heterogeneity, label scarcity, and domain variability. In this work, we introduce FoundAna, a GNN-assisted Foundation Model for Graph Anomaly Detection - the first foundation model framework designated for generalizable, cross-graph anomaly detection by combining GNNs and transformers. FoundAna integrates an anomaly detection-specific GNN component with a standard transformer encoder augmented by