arXiv Machine Learning

Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

The paper introduces a statistical feature augmentation technique that encodes behavioral interaction statistics into the input space for dynamic graph anomaly detection. Experiments on Reddit, Wikipedia, and MOOC datasets across seven models—both continuous-time and discrete-time—show that this augmentation consistently improves detection performance compared to models trained on original embeddings. The enriched input also facilitates fine-grained post-hoc analysis of behavioral importance, linking classical network analysis with deep learning.

arXiv Machine Learning

Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer

rag diffusion

arXiv AI

Sep 1

Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Experts

arXiv:2602.06859v3 Announce Type: replace-cross Abstract: Graph Anomaly Detection (GAD) aims to identify irregular patterns in graph data, and recent works have explored zero-shot generalist GAD to e...

By Xinyu Zhao, Qingyun Sun, Jiayi Luo, Xingcheng Fu, Jianxin Li

rag benchmarks safety

arXiv Machine Learning

Jun 2

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.

By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma

benchmarks

arXiv Machine Learning

Jun 19

We Need to Rethink Benchmarking in Anomaly Detection

arXiv:2507. 15584v2 Announce Type: replace Abstract: Despite the continuous proposal of new anomaly detection algorithms and extensive benchmarking efforts, progress seems to stagnate, with only minor performance differences between established baselines and new algorithms.

By Philipp R\"ochner, Simon Kl\"uttermann, Kevin Kammler, Franz Rothlauf, Emmanuel M\"uller, Daniel Schl\"or

benchmarks

arXiv Machine Learning

Jun 2

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems

arXiv:2606. 01691v1 Announce Type: cross Abstract: Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents.

By Yuchen Zhang, Ning Xi, Pengbin Feng, Shigang Liu, Jianfeng Ma, Yulong Shen, Yanan Sun, Xiaolin Zhou

llms robotics multimodal benchmarks safety

arXiv AI

Jul 20

Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.

By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim

arXiv AI

Aug 10

FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognition

arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).

By Ghani Haider, Majid Kundroo, Boyun Eom, Dong Hwan Park, Chen Chen, Taehong Kim

llms multimodal benchmarks safety

arXiv AI

Jun 9

XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder for O-RAN Traffic Anomaly Detection

arXiv:2502. 09194v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) techniques have become integral part in advancing next generation wireless communication systems by enabling sophisticated data modeling and feature extraction for enhanced network performance.

By Osman Tugay Basaran, Falko Dressler

safety

arXiv AI

Jun 2

Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.

By Yuxing Tian, Yiyan Qi, Fengran Mo, Weixu Zhang, Jian Guo, Jian-Yun Nie

arXiv Machine Learning

Jul 23

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

arXiv:2508. 00909v2 Announce Type: replace Abstract: Time series anomaly detection plays a critical role in a wide range of real-world applications.

By Aitor S\'anchez-Ferrera, Usue Mori, Borja Calvo, Jose A. Lozano

benchmarks

arXiv Machine Learning

Aug 6

Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection

arXiv:2608. 04753v1 Announce Type: new Abstract: Attention layers are the backbone of today's most powerful and impactful models.

By Mihailo Ili\'c, Milo\v{s} Savi\'c, Vladimir Kurbalija, Mirjana Ivanovi\'c, Giancarlo Fortino, Du\v{s}an Jakoveti\'c

llms

arXiv AI

Aug 25

Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings

arXiv:2507.12295v2 Announce Type: replace-cross Abstract: Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation id...

By Feng Xiao, Jicong Fan

llms rag nlp benchmarks

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