# Explaining Anomalies Detected by Autoencoders Using SHAP

arXiv: https://arxiv.org/abs/1903.02407 · HTML: https://mlsanity.com/paper/1903.02407/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 27 of 27)

- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2207.06236/) (2022)
- [DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications](https://mlsanity.com/paper/2109.11495/) (2021)
- [Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)](https://mlsanity.com/paper/2112.08442/) (2021)
- [Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model](https://mlsanity.com/paper/2308.00074/) (2023)
- [Explainable Machine Learning for Fraud Detection](https://mlsanity.com/paper/2105.06314/) (2021)
- [Ensembles of Random SHAPs](https://mlsanity.com/paper/2103.03302/) (2021)
- [A general-purpose method for applying Explainable AI for Anomaly Detection](https://mlsanity.com/paper/2207.11564/) (2022)
- [An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data](https://mlsanity.com/paper/2106.09111/) (2021)
- [Trustworthy Anomaly Detection: A Survey](https://mlsanity.com/paper/2202.07787/) (2022)
- [Towards Responsible AI for Financial Transactions](https://mlsanity.com/paper/2206.02419/) (2022)
- [A new interpretable unsupervised anomaly detection method based on residual explanation](https://mlsanity.com/paper/2103.07953/) (2021)
- [Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data](https://mlsanity.com/paper/2209.10658/) (2022)
- [RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations](https://mlsanity.com/paper/2209.09157/) (2022)
- [Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations](https://mlsanity.com/paper/2210.04145/) (2022)
- [Interpretable Ensembles of Hyper-Rectangles as Base Models](https://mlsanity.com/paper/2303.08625/) (2023)
- [Interpretation of multi-label classification models using shapley values](https://mlsanity.com/paper/2104.10505/) (2021)
- [Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2303.01259/) (2023)
- [Toward Faithful Explanations in Acoustic Anomaly Detection](https://mlsanity.com/paper/2601.12660/) (2026)
- [Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning](https://mlsanity.com/paper/2110.10038/) (2021)
- [Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces](https://mlsanity.com/paper/2209.04173/) (2022)
- [Unlocking Layer-wise Relevance Propagation for Autoencoders](https://mlsanity.com/paper/2303.11734/) (2023)
- [Detection of Sensor-To-Sensor Variations using Explainable AI](https://mlsanity.com/paper/2306.10850/) (2023)
- [Analyzing Key Users' behavior trends in Volunteer-Based Networks](https://mlsanity.com/paper/2310.05978/) (2023)
- [Can I trust my anomaly detection system? A case study based on explainable AI](https://mlsanity.com/paper/2407.19951/) (2024)
- [Explainable Threat Attribution for IoT Networks Using Conditional SHAP and Flow Behavior Modelling](https://mlsanity.com/paper/2603.22771/) (2026)
- [Witnesses Explain Anomalies](https://mlsanity.com/paper/2609.03826/) (2026)

## Abstract (arXiv)

Anomaly detection algorithms are often thought to be limited because they don't facilitate the process of validating results performed by domain experts. In Contrast, deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal cases in order to find anomalies. Most outlier detection algorithms output a score for each instance in the database. The top-k most intense outliers are returned to the user for further inspection; however the manual validation of results becomes challenging without additional clues. An explanation of why an instance is anomalous enables the experts to focus their investigation on most important anomalies and may increase their trust in the algorithm. Recently, a game theory-based framework known as SHapley Additive exPlanations (SHAP) has been shown to be effective in explaining various supervised learning models. In this research, we extend SHAP to explain anomalies detected by an autoencoder, an unsupervised model. The proposed method extracts and visually depicts both the features that most contributed to the anomaly and those that offset it. A preliminary experimental study using real world data demonstrates the usefulness of the proposed method in assisting the domain experts to understand the anomaly and filtering out the uninteresting anomalies, aiming at minimizing the false positive rate of detected anomalies.