mlsanityData to 14 Oct 2026

Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey

arXiv 2503.13195 ↗First posted 17 Mar 2025cs.LGcited by 17

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Abstract (arXiv)

The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets become more complex and high-dimensional, traditional detection methods struggle to effectively capture intricate patterns. Advances in deep learning have made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data. This survey provides a comprehensive review of over 180 recent studies, focusing on deep learning-based AD techniques. We categorize and analyze these methods into reconstruction-based and prediction-based approaches, highlighting their effectiveness in modeling complex data distributions. Additionally, we explore the integration of traditional and deep learning methods, highlighting how hybrid approaches combine the interpretability of traditional techniques with the flexibility of deep learning to enhance detection accuracy and model transparency. Finally, we identify open issues and propose future research directions to advance the field of AD. This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 58 of its 208 references that are in the map. First posted on arXiv 17 Mar 2025.

Builds on

58 of 208 references.

References · 58

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38 more
  1. 2022Diffusion Models for Medical Anomaly Detectioncited by 232
  2. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  3. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  4. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
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  6. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
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  14. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  15. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  16. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  17. 2020Anomaly Detection with Density Estimationcited by 72
  18. 2020Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumescited by 67
  19. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  20. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 64
  21. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 14.

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  1. 2025Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMscited by 2
  2. 2026One-to-More: High-Fidelity Training-Free Anomaly Generation with Attention Controlcited by 2
  3. 2025Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPCcited by 1
  4. 2026Latent Diffusion for Internet of Things Attack Data Generation in Intrusion Detectioncited by 1
  5. 2026On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systemscited by 1
  6. 2026Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detectioncited by 1
  7. 2026CLOE: Christoffel Loss Autoencoder for Anomaly Detectioncited by 1
  8. 2025Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyondcited by 0
  9. 2026Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detectioncited by 0
  10. 2026Detecting Abnormal User Feedback Patterns through Temporal Sentiment Aggregationcited by 0
  11. 2026VAE-Inf: A statistically interpretable generative paradigm for imbalanced classificationcited by 0
  12. 2026Characterizing Detectability in 3DGS Poisoning: A Stage-wise Benchmarkcited by 0
5 more
  1. 2026RareSense: Rarity-Aware Similarity Search for Anomaly Retrieval in Transactional Datacited by 0
  2. 2026LLM as Detector: An In-context Learning Approach for Tabular Anomaly Detectioncited by 0
  3. 2026Feature Transformation Enhanced Jacobi Polynomial Graph Filtering for Graph Anomaly Detectioncited by 0
  4. 2026SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systemscited by 0
  5. 2026CORE: Contrast-and-Reflect for Self-Supervised Prompt Optimization in Anomaly Synthesiscited by 0