Hugging Face Trending Papers
RAD: Rule-Augmented Relational Anomaly Detection
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arXiv Machine Learning
Jun 18
Towards Anomaly Detection on Relational Data
arXiv:2606. 18621v1 Announce Type: new Abstract: Relational databases are widely used for managing structured data in real-world systems.
Hugging Face Trending Papers
Jun 17
Towards Anomaly Detection on Relational Data
Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial for identifying fraud, risks, and abnormal behaviors, yet remains under-explored.
arXiv Machine Learning
Oct 23
One Pretrained Decision Model Is Enough for Training-Free Generalist Graph Anomaly Detection
The paper introduces Jev-GAD, a training‑free generalist graph anomaly detection framework that combines complementary structural, attribute, and neighborhood evidence with a single frozen pretrained language model for anomaly assessment. It transforms heterogeneous statistics into graph‑relative percentiles and uses a semantic Graph‑to‑State (G2S) transformation to feed evidence into a non‑generative decision readout, fusing the resulting scores with four statistical measures. Experiments on seven benchmark datasets show Jev‑GAD achieving high AUROC (89.18%) and AUPRC (52.40%) scores, demonstrating the effectiveness of pretrained decision readout and semantic evidence grounding.
arXiv Machine Learning
Jul 30
When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection
arXiv:2605. 26171v2 Announce Type: replace Abstract: Many practical anomalies are not merely rare inputs, but violations of semantic constraints: objects co-occur in structured ways, actions imply preconditions, and events satisfy temporal or relational regularities.
arXiv Machine Learning
Jun 9
A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection
arXiv:2510. 26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience.
arXiv Machine Learning
Oct 19
Universal Semantic Enhancement for Weakly Supervised Anomaly Detection
The paper introduces a weakly supervised anomaly detection approach for tabular data that leverages universal semantic knowledge from a pre-trained foundation model and domain-specific features learned from the target dataset. A prototype‑conditioned modulation mechanism fuses these complementary representations, enabling them to reinforce each other for better anomaly detection. Experiments on public datasets demonstrate that the method outperforms existing competitors when only eight labeled anomalies are used during training.