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Log analysis is one of the main techniques engineers use to troubleshootfaults of large-scale software systems. During the past decades, many loganalysis approaches have been proposed to detect system anomalies reflected bylogs. They usually take log event counts or sequential log events as inputs andutilize machine learning algorithms including deep learning models to detectsystem anomalies. These anomalies are often identified as violations ofquantitative relational patterns or sequential patterns of log events in logsequences. However, existing methods fail to leverage the spatial structuralrelationships among log events, resulting in potential false alarms andunstable performance. In this study, we propose a novel graph-based log anomalydetection method, LogGD, to effectively address the issue by transforming logsequences into graphs. We exploit the powerful capability of Graph TransformerNeural Network, which combines graph structure and node semantics for log-basedanomaly detection. We evaluate the proposed method on four widely-used publiclog datasets. Experimental results show that LogGD can outperformstate-of-the-art quantitative-based and sequence-based methods and achievestable performance under different window size settings. The results confirmthat LogGD is effective in log-based anomaly detection.

LogGD:Detecting Anomalies from System Logs by Graph Neural Networks