arXiv:2607. 26704v1 Announce Type: cross Abstract: Anomaly detection methods often have uncertain behavior with respect to samples near the distribution boundary, limiting their ability to anticipate future anomalies.
arXiv:2605. 13642v2 Announce Type: replace-cross Abstract: Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation.
arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.
arXiv:2402. 16388v4 Announce Type: replace-cross Abstract: The need for uncertainty quantification in anomaly detection systems has become increasingly important.
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition introduces C-PP-COAD, a framework that uses synthetic calibration data to reduce reliance on real-world calibration while maintaining assumption-free false discovery rate control. The method wraps any anomaly detection algorithm, converting its scores into conformal p-values for online testing. Experiments on synthetic and real datasets—including thyroid dysfunction, O‑RAN conflict, 5G intrusion, and UE throughput degradation—show that C-PP-COAD preserves FDR guarantees while significantly cutting the need for real calibration data.
arXiv:2608. 05685v1 Announce Type: new Abstract: Most public benchmarks for machine-condition monitoring come from test rigs, where faults are induced on purpose and every event is known.
arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...
arXiv:2608. 10587v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings.
arXiv:2601. 02610v3 Announce Type: replace-cross Abstract: Novelty detection via conformal $p$-values and BH procedure provides distribution-free global false discovery rate (FDR) control.
CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.
The paper investigates whether the performance of anomaly detection systems can be predicted without labeled anomalies. For kNN-based detectors, it derives a lower bound on AUC that links detection performance to the separation and variance of inlier and outlier scores, and uses this to analyze how density variation, intrinsic dimensionality, and domain mismatch affect score variability. The authors introduce pseudo‑anomaly probes that provide a reference for estimating relative score separation, and demonstrate through experiments on DCASE benchmarks that these probes enable anomaly‑free model selection to outperform conventional development‑set selection, especially under domain shift.
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.
arXiv:2606. 00052v1 Announce Type: new Abstract: As Industry 4.