Mining causes of network events in log data with causal inference

Network log message (e.g., syslog) is valuable information to detect unexpected or anomalous behavior in a large scale network. However, pinpointing failures and their causes is not an easy problem because of a huge amount of system log data in daily operation. In this study, we propose a method extracting failures and their causes from network syslog data. The main idea of the method relies on causal inference that reconstructs causality of network events from a set of the time series of events. Causal inference allows us to reduce the number of correlated events by chance, thus it outputs more plausible causal events than a traditional cross-correlation based approach. We apply our method to 15 months network syslog data obtained in a nation-wide academic network in Japan. Our method significantly reduces the number of pseudo correlated events compared with the traditional method. Also, through two case studies and comparison with trouble ticket data, we demonstrate the effectiveness of our method for network operation.