OpenLog: Incremental Anomaly Classification with Changing, Unbalanced and Unknown Logs.

With the development of Artificial Intelligence for IT Operations(AIOps), an increasing number of log analysis approaches have been proposed to guarantee the stability and reliability of large-scale systems. However, traditional approaches still exist some limitations. First, they cannot adapt to the changing log statements. Second, most of them are based on large-scale labeled data. Third, most of their frames are fixed. To overcome these limitations, we propose OpenLog based on meta-learning and few-shot learning. Experimental results show that OpenLog can achieve an accuracy of 95.8% on detecting unknown anomaly classes, and an accuracy of 93.3% in anomaly classification on unbalanced logs with unknown anomaly classes. In the few-shot classification, OpenLog can still achieve an accuracy over 80%.