LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs

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…

• NN
  National Natural Science Foundation of China

  Awards: 61402257, 61772307
• FR
  Fundamental Research Funds for the Central Universities