Temporal Patterns Discovery of Evolving Graphs for Graph Neural Network (GNN)-based Anomaly Detection in Heterogeneous Networks

Jongmo Kim, Kunyoung Kim, Gi-Yoon Jeon, Mye M. Sohn
2022 Journal of Internet Services and Information Security  
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 patterns
more » ... r the snapshots using the subgraph embedding technique and gaussianbase KL divergence. At the time, the association method finds intra-snapshots and inter-snapshots associations. As a result, we can obtain an evolving graph that is simplified and temporal patternsenhanced from original networks. It is used an input graph for a GNN-based anomaly detection model. To show the superiority of the proposed framework, we conduct experiments and evaluations on 8 real-world datasets with anomaly labels with comparative state-of-the-art models of graph-based anomaly detection. We show that the proposed framework outperforms state-of-the-art methods in the accuracy and stability of training with the trend of decreasing train loss.
doi:10.22667/jisis.2022.02.28.072 dblp:journals/jisis/KimKJS22 fatcat:uncpemjenbgavni65vrnhzszzy