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LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs
Weibin Meng (Beijing National Research Center for Information Science and Technology, Tsinghua University), Ying Liu (Nankai University, Beijing National Research Center for Information Science and Technology), Yichen Zhu (University of Toronto), Shenglin Zhang (Nankai University), Dan Pei (Beijing National Research Center for Information Science and Technology, Tsinghua University), Yuqing Liu (Nankai University, Beijing National Research Center for Information Science and Technology), Yihao Chen (Beijing National Research Center for Information Science and Technology, Tsinghua University), Ruizhi Zhang (Huawei Technologies (China)), Shimin Tao (Huawei Technologies (China)), Pei Sun (Huawei Technologies (China)), Rong Zhou (Huawei Technologies (China))
Résumé : Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.