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K^4: Online Log Anomaly Detection via Unsupervised Typicality Learning (arxiv.org)
3 points by barthelomew 12 days ago | hide | past | favorite | 1 comment




> Abstract: [...] (K^4) transforms arbitrary log embeddings into compact four-dimensional descriptors (Precision, Recall, Density, Coverage) using efficient k-nearest neighbor (k-NN) statistics. These descriptors enable lightweight detectors to accurately score anomalies without retraining. Using a more realistic online evaluation protocol, sets a new state-of-the-art (AUROC: 0.995-0.999), outperforming baselines by large margins while being orders of magnitude faster, with training under 4 seconds and inference as low as 4 us.



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