# A Survey on Explainable Anomaly Detection

arXiv: https://arxiv.org/abs/2210.06959 · HTML: https://mlsanity.com/paper/2210.06959/

## 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 (most cited first, 30 of 66)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Explainable Deep Few-shot Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/2108.00462/) (2021)

## Built on by (most cited first, 30 of 39)

- [Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2409.01980/) (2024)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Explainable Contextual Anomaly Detection using Quantile Regression Forests](https://mlsanity.com/paper/2302.11239/) (2023)
- [LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions](https://mlsanity.com/paper/2503.20252/) (2025)
- [Graph Neural Networks based Log Anomaly Detection and Explanation](https://mlsanity.com/paper/2307.00527/) (2023)
- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)
- [Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos](https://mlsanity.com/paper/2302.06670/) (2023)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction](https://mlsanity.com/paper/2502.10211/) (2025)
- [Enhancing Interpretability and Generalizability in Extended Isolation Forests](https://mlsanity.com/paper/2310.05468/) (2023)
- [Agentic AI for autonomous anomaly management in complex systems](https://mlsanity.com/paper/2507.15676/) (2025)
- [From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection](https://mlsanity.com/paper/2511.20500/) (2025)
- [AcME-AD: Accelerated Model Explanations for Anomaly Detection](https://mlsanity.com/paper/2403.01245/) (2024)
- [Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review](https://mlsanity.com/paper/2411.14278/) (2024)
- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)
- [Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts](https://mlsanity.com/paper/2509.26227/) (2025)
- [Explainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networks](https://mlsanity.com/paper/2601.09287/) (2026)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Black-Box Anomaly Attribution](https://mlsanity.com/paper/2305.18440/) (2023)
- [Outlier detection using flexible categorisation and interrogative agendas](https://mlsanity.com/paper/2312.12010/) (2023)
- [Flexible categorization using formal concept analysis and Dempster-Shafer theory](https://mlsanity.com/paper/2408.15012/) (2024)
- [Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine](https://mlsanity.com/paper/2409.00155/) (2024)
- [Interactive Explainable Anomaly Detection for Industrial Settings](https://mlsanity.com/paper/2410.12817/) (2024)
- [A Feature Engineering Approach for Business Impact-Oriented Failure Detection in Distributed Instant Payment Systems](https://mlsanity.com/paper/2510.21710/) (2025)
- [Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies](https://mlsanity.com/paper/2511.03095/) (2025)
- [Toward Faithful Explanations in Acoustic Anomaly Detection](https://mlsanity.com/paper/2601.12660/) (2026)
- [Abnormal component analysis](https://mlsanity.com/paper/2312.16139/) (2023)
- [MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring](https://mlsanity.com/paper/2409.13602/) (2024)
- [Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection](https://mlsanity.com/paper/2501.02069/) (2025)
- [Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks](https://mlsanity.com/paper/2501.06571/) (2025)

## 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.