Anomaly Detection

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40 papers in the last four weeks, up 33% on the four weeks before. 0.4% of all new papers.

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Latest papers 409

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GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges
May 8, 2026Jingjing Zhou, Shiyu Huang, Qing Qing +7Graph Neural Networks Graph Representation Learning
Zhejiang Gongshang University · Jilin University · Adelaide University +2
Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
May 7, 2026Victor Livernoche, Jie Zan, Reihaneh RabbanyTabular Anomaly Detection Unsupervised Anomaly Detection
McGill University, Mila, Montréal, Canada
Smart Railway Obstruction Detection System using IoT and Computer Vision
May 7, 2026Pravin Kumar, Mritunjay Shall Peelam, Ramakant Kumar +2Internet of Things Anomaly Detection
School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun, 248001, Uttarakhand, India · Department of Computer Science, Galgotias College of Engineering & Technology · Department of Computer Science, NIT Jamshedpur +2
Conditional outlier detection for clinical alerting
May 6, 2026Milos Hauskrecht, Michal Valko, Shyam Visweswaran +3Healthcare Clinical Decision Support
Computer Science Department · Department of Biomedical Informatics · Department of Critical Care Medicine
Evidence-based anomaly detection in clinical domains
May 6, 2026Milos Hauskrecht, Michal Valko, Branislav Kveton +2Clinical Decision Support Clinical Decision-Making
Computer Science Department; 2 Intelligent Systems Program; 3 Department of Biomedical Informatics, University of Pittsburgh, PA · Intelligent Systems Program; 3 Department of Biomedical Informatics, University of Pittsburgh, PA
CCL-D: A High-Precision Diagnostic System for Slow and Hang Anomalies in Large-Scale Model Training
May 6, 2026Yida Gu, Fakang Wang, Jianhao Fu +17Anomaly Localization Distributed Training
University of Chinese Academy of Sciences · Ant Group · Jilin University
Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning
May 5, 2026Michal ValkoGraph-Based Semi-Supervised Learning Semi-Supervised Learning
Comenius University, Bratislava · University of Pittsburgh
MEMSAD: Gradient-Coupled Anomaly Detection for Memory Poisoning in Retrieval-Augmented Agents
May 5, 2026Ishrith GowdaRAG Poisoning Attacks Agent Memory Poisoning
University of California, Berkeley · Department of Electrical Engineering and Computer Sciences University of California, Berkeley · Work conducted at the Song Lab, Berkeley AI Research, University of California, Berkeley.
Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection
May 5, 2026Haibo Xiao, Hanzhe Liang, Jie Zhou +2Point Cloud Anomaly Detection Signed Distance Functions
College of Computer Science and Software Engineering, Shenzhen University · Biological and Life Sciences Division, Mohamed bin Zayed University of Artificial Intelligence, UAE · Shenzhen Audencia Financial Technology Institute, Shenzhen University, China +1
LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing
May 5, 2026Ahmadreza Eslaminia, Chuhan Cai, Cameron Smith +5Multi-Agent LLM Systems Industrial Anomaly Detection
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA +1
Unsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics
May 4, 2026Bogdan OanceaTabular Anomaly Detection Unsupervised Anomaly Detection
Department of Applied Economics and Quantitative Analysis, University of Bucharest, Romania · National Institute for Research and Development for Biological Sciences, Bucharest, Romania
Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models
May 4, 2026Sandra Arcos-Holzinger, Sarah M. Erfani, James Bailey +1Intrinsic Dimensionality Unsupervised Anomaly Detection
University of Melbourne, School of Computing and Information Systems, Australia · Monash University, Department of Data Science and Artificial Intelligence, Australia · Johns Hopkins University, Center for Language and Speech Processing, USA
Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection
May 4, 2026Fuyun Wang, Yuanzhi Wang, Xu Guo +6Flow Matching Prototype Learning
Nanjing University of Science and Technology, Nanjing, China · Beijing Normal University, Beijing, China · China Academy of Space Technology, Beijing, China +1
Energy-Based Constraint Networks: Learning Structural Coherence Across Modalities
May 1, 2026Chirag ShindeEnergy-Based Models Anomaly Detection
Independent Researcher
Distance metric learning for conditional anomaly detection
May 1, 2026Michal Valko, Milos HauskrechtMetric Learning Anomaly Detection
University of Pittsburgh, PA
Scalable Context-Aware Graph Attention for Unsupervised Anomaly Detection in Large-Scale Mobile Networks
May 1, 2026Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2Graph Attention Networks Reconstruction-Based Anomaly Detection
Telenor Research and Innovation, Norway · Norwegian University of Science and Technology (NTNU), Norway · Hance, Norway +2
EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems
May 1, 2026Luan Pham, Victor Nicolet, Joey Dodds +2Online Anomaly Detection Anomaly Localization
RMIT University, Australia · Amazon Web Services, United States
PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs
Apr 30, 2026Raviteja Bommireddy, Varshith Bandaru, Lohith Pakala +1Cyber-Physical Systems Frequency-Domain Feature Learning
Department of CSE IIITDM Kancheepuram Chennai, India
Connected Dependability Cage: Run-Time Function and Anomaly Monitoring for the Development and Operation of Safe Automated Vehicles
Apr 30, 2026Iqra Aslam, Nour Habib, Abhishek Buragohain +4Autonomous Driving Perception Runtime Verification
Institute for Software and System Engineering, Clausthal University of Technology, Germany
Context-Aware Graph Attention for Unsupervised Telco Anomaly Detection
Apr 29, 2026Sara Malacarne, Eirik Hoel-Høiseth, Erlend Aune +2Graph Attention Networks Reconstruction-Based Anomaly Detection
Telenor Research and Innovation, Norway · Norwegian University of Science and Technology (NTNU), Norway · Hance, Norway +2
Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection
Apr 29, 2026Jinye Gan, Bozhong Zheng, Xiaohao Xu +4Point Cloud Anomaly Detection Anomaly Detection
ShanghaiTech University · China · University of Michigan, Ann Arbor +1
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Apr 29, 2026Qi Li, Yong Huang, Hui LiConditional Diffusion Models Structural Health Monitoring
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, 150090, China
Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
Apr 29, 2026Abdul Joseph Fofanah, Lian Wen, David Chen +2Graph Neural Networks Spiking Neural Networks
School of ICT, Griffith University, Australia
Enforcing Benign Trajectories: A Behavioral Firewall for Structured-Workflow AI Agents
Apr 29, 2026Hung DangLLM Agent Security AI Agent Monitoring
Van Lang University, Ho Chi Minh City, Vietnam
Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships
Apr 29, 2026Yuhan Wang, Ruobing Yan, Zhe Su +3Anomaly Localization Reconstruction-Based Anomaly Detection
Columbia University, New York, USA · Georgetown University, Washington, D.C., USA · Pepperdine University, Malibu, USA +2
Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching
Apr 28, 2026Yuhe Bai, Chengli Tan, Jiaqi Li +2Dynamical Systems Regime-Switching Dynamical Systems
School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, China · School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710072, China
The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive
Apr 28, 2026Alex Bogdan, Adrian de Valois-FranklinLLM Evaluation Language Model Generation Evaluation
Evolutionairy AI Toronto, Canada
Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing
Apr 27, 2026Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami +4Structural Health Monitoring Anomaly Detection
NEC Corporation, Tokyo, Japan
Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks
Apr 27, 2026Lidia Losavio, Luca Persia, Madan Sathe +1Financial Fraud Detection Spatiotemporal GNNs
Faculty of Informatics, Universit`a della Svizzera italiana, Lugano, Switzerland · School of Management and Law, Zurich University of Applied Sciences, Zurich, Switzerland · Forensics and Financial Crime, Deloitte AG, Switzerland
Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models
Apr 27, 2026Nay Myat Min, Long H. Pham, Jun SunLLM Backdoor Attacks Adversarial Attacks on LLMs
Singapore Management University

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GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges

Jingjing Zhou, Shiyu Huang, Qing Qing, Zuquan Yuan+6

Zhejiang Gongshang University · Jilin University · Adelaide University+2

Graph Neural Networks Graph Representation Learning Learning with Missing Data Anomaly Detection

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Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform governance. However, existing GAD benchmarks are often restricted to small-scale, curated graphs with relatively balanced anomaly ratios, leaving a substantial gap between academic evaluation and real-world deployment. To bridge this gap, we present a multi-dimensional benchmark that systematically evaluates GAD models under three deployment-relevant challenges: million-scale graphs, extreme anomaly scarcity, and missing node attributes. We derive a family of controlled benchmark variants from five diverse graphs, including two native industrial-scale datasets with over 3.7 million nodes. Our extensive evaluation of nine representative GAD models reveals three major limitations: (1) most GNN-based methods fail to scale to million-node graphs due to prohibitive memory requirements; (2) detection performance drops sharply under realistic anomaly ratios (e.g., 0.1%), often resulting in zero recall; and (3) reconstruction-based models are highly sensitive to attribute imputation strategies. Our findings suggest that strong performance in laboratory settings does not guarantee robustness in production environments. We release this benchmark and empirical evaluation as a diagnostic testbed to promote the development of robust and scalable GAD systems for large-scale, imperfect graphs encountered in practice. Code is available at https://anonymous.4open.science/r/Benchmark_GAD-E7A3.

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Latest papers 409