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

arXiv:2608.17965 · cs.LG, cs.AI, cs.SE · Submitted 2026-08-18 · Read on arXiv

cs.LG, cs.AI, cs.SE

Submitted: 2026-08-18

Updated: 2026-09-25

Comments: Accepted at the 2026 IEEE International Conference on Data Mining (ICDM 2026)

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 105× in this paper · explained in 7 episodes
• Brier Score — 10× in this paper
• Accuracy — 8× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Latent Representations — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Representation Learning — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Latent Space — 2× in this paper · explained in 7 episodes
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Accuracy Alignment — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Alert Fatigue — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Confidence on Error — 1× in this paper
• Digital Twin — 1× in this paper · explained in 4 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Expected Calibration Error — 1× in this paper
• F1 Score — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Monte Carlo dropout — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes

Sources

• LogLLM: Log-based Anomaly Detection Using Large Language Models
• Regularizing Neural Networks by Penalizing Confident Output Distributions

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