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Anomaly detection becomes increasingly important for the dependability andserviceability of IT services. As log lines record events during the executionof IT services, they are a primary source for diagnostics. Thereby,unsupervised methods provide a significant benefit since not all anomalies canbe known at training time. Existing unsupervised methods need anomaly examplesto obtain a suitable decision boundary required for the anomaly detection task.This requirement poses practical limitations. Therefore, we develop A2Log,which is an unsupervised anomaly detection method consisting of two steps:Anomaly scoring and anomaly decision. First, we utilize a self-attention neuralnetwork to perform the scoring for each log message. Second, we set thedecision boundary based on data augmentation of the available normal trainingdata. The method is evaluated on three publicly available datasets and oneindustry dataset. We show that our approach outperforms existing methods.Furthermore, we utilize available anomaly examples to set optimal decisionboundaries to acquire strong baselines. We show that our approach, whichdetermines decision boundaries without utilizing anomaly examples, can reachscores of the strong baselines.

A2Log: Attentive Augmented Log Anomaly Detection