The paper investigates how different adaptation strategies, model architectures, parameter scales, and quantization settings influence the performance, efficiency, and robustness of large language models (LLMs) for log anomaly detection. Across three public log datasets, the study finds that adaptation strategies lead to significant performance variations, model scaling offers dataset‑dependent gains, and models with similar accuracy can differ markedly in computational cost. Low‑bit quantization largely preserves detection performance, and the authors also assess robustness to structural, semantic, and label noise at varying perturbation levels.
By Bin Li, Dongdong Wang, Siyang Lu
The paper introduces LoRD, a lightweight post‑hoc calibration framework designed to improve confidence reliability in language‑model‑based log anomaly detectors. LoRD learns route‑specific reliability models from latent representations of correctly classified validation samples and uses reconstruction distances to estimate prediction reliability. By selectively recalibrating high‑risk predictions, LoRD reduces overconfident errors while maintaining strong anomaly detection performance across four large‑scale log benchmark datasets.
By Bin Li, Dongdong Wang, Siyang Lu
arXiv:2606. 04957v1 Announce Type: cross Abstract: System-generated logs underpin security monitoring, yet their rigid template-based format hinders both automated analysis and human comprehension.
By Samuel Ndichu, Tao Ban, Seiichi Ozawa, Takeshi Takahashi, Daisuke Inoue
arXiv:2608. 19938v1 Announce Type: cross Abstract: Existing approaches to anomalous behaviour log detection, such as Wazuh rely primarily on predefined detection rules, while statistical anomaly detection approaches such as OpenSearch identify deviations from previously observed behavioural patterns.
By Christopher Henshaw, Gour Karmakar
TrajMind is a framework for diagnosing collective anomalies in urban trajectory data. It separates continuous screening from on-demand diagnosis, using a fast text-only path for alerts and a slow vision‑language path that chains role‑specialized LoRA adapters for detailed, evidence‑backed what‑who‑where‑when records. Experiments show the slow path outperforms baselines by over 15 percentage points in typing and 13 in localization, while the fast path cuts latency by 41% and retains high accuracy.
By Jiahao Wu, Zhenqun Yang, Chen Jason Zhang, Qing Li
arXiv:2608. 19463v1 Announce Type: new Abstract: Anomaly detection in tabular data is challenging because abnormal samples often arise as violations of cross-feature dependencies rather than simple marginal deviations.
By Tu Anh Hoang Nguyen, Dang Nguyen, Thuc Duy Le, Trung Le, Sunil Gupta