PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning

dc.contributor.author Wittkopp, Thorsten
dc.contributor.author Scheinert, Dominik
dc.contributor.author Wiesner, Philipp
dc.contributor.author Acker, Alexander
dc.contributor.author Kao, Odej
dc.date.accessioned 2022-12-27T18:58:38Z
dc.date.available 2022-12-27T18:58:38Z
dc.date.issued 2023-01-03
dc.description.abstract Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data such as logs allows gaining relevant insights to improve IT services steadily and eradicate failures. However, existing anomaly detection methods that provide high accuracy often rely on labeled training data, which are time-consuming to obtain in practice. Therefore, we propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows provided by monitoring systems instead of labeled data. Our attention-based model uses a novel objective function for weak supervision deep learning that accounts for imbalanced data and applies an iterative learning strategy for positive and unknown samples (PU learning) to identify anomalous logs. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets and detects anomalous log messages with an F1-score of more than 0.99 even within imprecise failure time windows.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.172
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102802
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject dependability
dc.subject log anomaly detection
dc.subject service reliability
dc.subject weak supervision
dc.title PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning
dc.type.dcmi text
prism.startingpage 1376
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