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Logs are widely used by large and complex software-intensive systems for troubleshooting. There have been a lot of studies on log-based anomaly detection. To detect the anomalies, the existing methods mainly construct a detection model using log event data extracted from historical logs. However, we find that the existing methods do not work well in practice. These methods have the close-world assumption, which assumes that the log data is stable over time and the set of distinct log events is known. However, our empirical study shows that in practice, log data often contains previously unseen log events or log sequences. The instability of log data comes from two sources: 1) the evolution of logging statements, and 2) the processing noise in log data. In this paper, we propose a new log-based anomaly detection approach, called LogRobust. LogRobust extracts semantic information of log events and …