Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection

T1Covered by 1 source · first reported by arXiv at 25 Sept, 04:00 UTC

arXiv:2608.17965v3 Announce Type: replace-cross Abstract: Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-based log anomaly detectors achieve strong detection performance, their confidence estimates remain…

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• T1Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly DetectionarXiv25 Sept, 04:00