mlsanityData to 14 Oct 2026

A Survey on Explainable Anomaly Detection

arXiv 2210.06959 ↗First posted 13 Oct 2022cs.LGcited by 39

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Abstract (arXiv)

In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). Builds on 66 of its 230 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

66 of 230 references.

References · 66

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  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
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38 more
  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  2. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  3. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 443
  4. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  5. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 365
  6. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  7. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
  8. 2019The many Shapley values for model explanationcited by 344
  9. 2019Interpretable machine learning: definitions, methods, and applicationscited by 305
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  11. 2020A Survey on the Explainability of Supervised Machine Learningcited by 227
  12. 2019Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systemscited by 205
  13. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  14. 2020Explainable Deep One-Class Classificationcited by 127
  15. 2020Principles and Practice of Explainable Machine Learningcited by 99
  16. 2017Recent Advances in Recurrent Neural Networkscited by 89
  17. 2022Anomaly Detection in Autonomous Driving: A Surveycited by 87
  18. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  19. 2018Contrastive Explanation: A Structural-Model Approachcited by 65
  20. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  21. 2019GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detectioncited by 36
  22. 2019Explaining Anomalies Detected by Autoencoders Using SHAPcited by 27
  23. 2020Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failurecited by 27
  24. 2018Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Modelscited by 24
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  27. 2019Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detectioncited by 19
  28. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 15
  29. 2021Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)cited by 15
  30. 2022Data-Efficient and Interpretable Tabular Anomaly Detectioncited by 15
  31. 2017Explaining Anomalies in Groups with Characterizing Subspace Rulescited by 13
  32. 2020The Clever Hans Effect in Anomaly Detectioncited by 13
  33. 2021Discrete neural representations for explainable anomaly detectioncited by 12
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  36. 2019Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMcited by 8
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50 most cited of 66 All 66 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 9; 2025: 16; 2026: 7.

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  1. 2024Large Language Models for Anomaly and Out-of-Distribution Detection: A Surveycited by 27
  2. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  3. 2023Explainable Contextual Anomaly Detection using Quantile Regression Forestscited by 10
  4. 2025LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questionscited by 9
  5. 2023Graph Neural Networks based Log Anomaly Detection and Explanationcited by 8
  6. 2024Interpretable Clustering: A Surveycited by 8
  7. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  8. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
  9. 2025Control-flow anomaly detection by process mining-based feature extraction and dimensionality reductioncited by 6
  10. 2023Enhancing Interpretability and Generalizability in Extended Isolation Forestscited by 5
  11. 2025Agentic AI for autonomous anomaly management in complex systemscited by 4
  12. 2025From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detectioncited by 3
27 more
  1. 2024AcME-AD: Accelerated Model Explanations for Anomaly Detectioncited by 2
  2. 2024Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Reviewcited by 2
  3. 2025A Survey on Interpretability in Visual Recognitioncited by 2
  4. 2025Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Expertscited by 2
  5. 2026Explainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networkscited by 2
  6. 2026FoMo X: Modular Explainability Signals for Outlier Detection Foundation Modelscited by 2
  7. 2023Black-Box Anomaly Attributioncited by 1
  8. 2023Outlier detection using flexible categorisation and interrogative agendascited by 1
  9. 2024Flexible categorization using formal concept analysis and Dempster-Shafer theorycited by 1
  10. 2024Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicinecited by 1
  11. 2024Interactive Explainable Anomaly Detection for Industrial Settingscited by 1
  12. 2025A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systemscited by 1
  13. 2025Sparse, self-organizing ensembles of local kernels detect rare statistical anomaliescited by 1
  14. 2026Toward Faithful Explanations in Acoustic Anomaly Detectioncited by 1
  15. 2023Abnormal component analysiscited by 0
  16. 2024MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoringcited by 0
  17. 2025Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detectioncited by 0
  18. 2025Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networkscited by 0
  19. 2025Explaining Anomalies with Tensor Networkscited by 0
  20. 2025Research on Anomaly Detection Methods Based on Diffusion Modelscited by 0
  21. 2025Towards Explainable Anomaly Detection in Shared Mobility Systemscited by 0
  22. 2025Transforming Credit Risk Analysis: A Time-Series-Driven ResE-BiLSTM Framework for Post-Loan Default Detectioncited by 0
  23. 2025Explainable Deep Convolutional Multi-Type Anomaly Detectioncited by 0
  24. 2026Root Cause Analysis of Measurement and Mechanistic Anomaliescited by 0
  25. 2026Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detectioncited by 0
  26. 2026Explainable Outlier Detection for Interval-valued Datacited by 0
  27. 2026Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Datacited by 0