The paper introduces methods for monotonic anomaly detection, focusing on anomalies that exhibit high (or low) attribute values rather than arbitrary deviations. It proposes an asymmetrical distance measure using a ramp function for distance-based methods and a modified path length algorithm for Isolation Forest. Experiments on synthetic and real-life datasets demonstrate improved detection performance on datasets with monotonic attributes.
By Oliver Urs Lenz, Matthijs van Leeuwen
The paper introduces ISER, an isolation-based method for unsupervised tabular anomaly detection that uses hypersphere radii to encode local density and maintains linear time and constant space complexity. ISER builds ensemble representations where smaller radii indicate dense regions and larger radii indicate sparse regions, and it employs a similarity-based scoring method that compares these representations to a theoretical anomaly reference pattern. Experiments on 20 real-world datasets show that ISER outperforms 12 state‑of‑the‑art methods, including an enhanced Isolation Forest.
By Yang Cao, Sikun Yang, Hao Tian, Kai He, Lianyong Qi, Ming Liu, Yujiu Yang, Hong-Kun Zhang
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.
By Kevin Wilkinghoff, Zheng-Hua Tan
arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.
By Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer, Saptarshi Bej
arXiv:2606. 13486v1 Announce Type: cross Abstract: Anomaly detection in multivariate time series is challenged by four structurally distinct anomaly types -- point (isolated spikes), distributional (level shifts), temporal (rhythm changes), and collective (inter-sensor correlation breakdowns) -- each requiring different feature representations.
By William Smits
arXiv:2606. 28970v1 Announce Type: cross Abstract: Unsupervised tabular anomaly detection requires methods that are accurate, robust across heterogeneous datasets, and computationally efficient.
By Quanling Zhao, Jiaying Yang, Ye Tian, Josh Victoria, Zhijun Wang, Pietro Mercati, Onat Gungor, Tajana Rosing
arXiv:2606. 18833v1 Announce Type: new Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection.
By Nassir Mohammad
arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.
By George Yamoah Afrifa, Theophilus Ansah-Narh, Marcellin Atemkeng
DIFFINT is a reconstruction‑based anomaly detector that replaces the opaque latent bottleneck of a standard autoencoder with a set of soft, axis‑aligned interval memberships learned directly from raw numerical data. Each latent unit represents a human‑readable hyper‑rectangle, and an instance’s anomaly score is its reconstruction error weighted by how strongly it falls inside these intervals. The method provides a certified lower bound on reconstruction error for points outside all active intervals, a graded suppression mechanism for sparse anomalies, and a closed‑form, label‑free importance ranking for each (unit, feature) pair, achieving top performance on 48 ADBench benchmarks against 22 baselines.
whyItMatters":"DIFFINT offers the first interpretable anomaly detector that maintains competitive performance while revealing which feature ranges drive each anomaly score, enabling practitioners to audit and understand model decisions without requiring anomaly labels."
DIFFINT is a reconstruction‑based anomaly detector that uses a differentiable autoencoder with a latent bottleneck composed of soft, axis‑aligned interval memberships. Each latent unit represents a human‑readable hyper‑rectangle in feature space, allowing the model to encode how strongly an instance falls inside each interval and to compute reconstruction error as the anomaly score. The method provides a certified lower bound on reconstruction error for points outside all active intervals, a suppression mechanism for sparse abnormalities, and a closed‑form, label‑free importance ranking for each (unit, feature) pair, achieving top performance on 48 ADBench benchmarks against 22 baselines.
By Lamine Diop, Marc Plantevit
arXiv:2608. 19801v1 Announce Type: new Abstract: Financial anomaly detection often relies on large unlabeled transaction logs, where anomalous samples may already be present during training.
By Philip Konz, Tejaswini Medi, Margret Keuper
AT3D-AD introduces a unified framework for detecting, localizing, and classifying 3D point‑cloud anomalies. It uses a Physics‑Driven Parametric Anomaly Synthesis module to generate synthetic defects for explicit supervision, a Hierarchical Global‑Local Anomaly Alignment module to refine representations, and a Semantic‑Geometric Anomaly Classification module to achieve precise, type‑discriminative localization. The method sets new state‑of‑the‑art results on four benchmarks, achieving high AUROC and Macro‑F1 scores.
By Jingyu Zeng, Haoquan Lu, Can Gao