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Health monitoring is important for maintaining reliable information andcommunications technology (ICT) systems. Anomaly detection methods based onmachine learning, which train a model for describing "normality" are promisingfor monitoring the state of ICT systems. However, these methods cannot be usedwhen the type of monitored log data changes from that of training data due tothe replacement of certain equipment. Therefore, such methods may dismiss ananomaly that appears when log data changes. To solve this problem, we proposean ICT-systems-monitoring method with deep learning models divided based on thecorrelation of log data. We also propose an algorithm for extracting thecorrelations of log data from a deep learning model and separating log databased on the correlation. When some of the log data changes, our method cancontinue health monitoring with the divided models which are not affected bychanges in the log data. We present the results from experiments involvingbenchmark data and real log data, which indicate that our method using dividedmodels does not decrease anomaly detection accuracy and a model for anomalydetection can be divided to continue monitoring a network state even if somethe log data change.

Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems