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A2Log: Attentive Augmented Log Anomaly Detection by Wittkopp, Thorsten; Acker, Alexander; Nedelkoski, Sasho; Bogatinovski, Jasmin; Scheinert, Dominik; Fan, Wu; Kao, Odej is a scholarly article available to read on EtoBox.

What is A2Log: Attentive Augmented Log Anomaly Detection about?

Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. As log lines record events during the execution of IT services, they are a primary source for diagnostics. Thereby, unsupervised methods provide a significant benefit since not all anomalies can be known at training time. Existing unsupervised methods need anomaly examples to 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 neural network to perform the scoring for each log message. Second, we set the decision boundary based on data augmentation of the available normal training data. The method is evaluated on three publicly available datasets and one industry dataset. We show that our approach outperforms existing methods. Furthermore, we utilize available anomaly examples to set optimal decision boundaries to acquire strong baselines. We show that our approach, which determines decision boundaries without utilizing anomaly exampl

Author

Wittkopp, Thorsten; Acker, Alexander; Nedelkoski, Sasho; Bogatinovski, Jasmin; Scheinert, Dominik; Fan, Wu; Kao, Odej

Published

2021

Language

EN