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Due to the complexity of modern IT services, failures can be manifold, occurat any stage, and are hard to detect. For this reason, anomaly detectionapplied to monitoring data such as logs allows gaining relevant insights toimprove IT services steadily and eradicate failures. However, existing anomalydetection methods that provide high accuracy often rely on labeled trainingdata, which are time-consuming to obtain in practice. Therefore, we proposePULL, an iterative log analysis method for reactive anomaly detection based onestimated failure time windows provided by monitoring systems instead oflabeled data. Our attention-based model uses a novel objective function forweak supervision deep learning that accounts for imbalanced data and applies aniterative learning strategy for positive and unknown samples (PU learning) toidentify anomalous logs. Our evaluation shows that PULL consistentlyoutperforms ten benchmark baselines across three different datasets and detectsanomalous log messages with an F1-score of more than 0.99 even within imprecisefailure time windows.

PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning