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Explaining Anomalies Detected by Autoencoders Using SHAP

6 March 2019

Liat Antwarg

Ronnie Mindlin Miller

Papers citing "Explaining Anomalies Detected by Autoencoders Using SHAP"

32 / 32 papers shown

Title
An Unsupervised Deep XAI Framework for Localization of Concurrent Replay Attacks in Nuclear Reactor Signals Konstantinos Vasili Zachery T. Dahm William Richards AAML 40 2 0 05 Aug 2025
Can I trust my anomaly detection system? A case study based on explainable AI Muhammad Rashid E. Amparore Enrico Ferrari Damiano Verda 122 0 0 29 Jul 2024
Approximating the Core via Iterative Coalition Sampling I. Gemp Marc Lanctot Luke Marris Yiran Mao Edgar A. Duénez-Guzmán ... Michael Kaisers Daniel Hennes Kalesha Bullard Kate Larson Yoram Bachrach 158 2 0 06 Feb 2024
Analyzing Key Users' behavior trends in Volunteer-Based Networks Nofar Piterman Tamar Makov Michael Fire 148 0 0 04 Oct 2023
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model Khushnaseeb Roshan Aasim Zafar 112 25 0 31 Jul 2023
Detection of Sensor-To-Sensor Variations using Explainable AI Sarah Seifi Sebastian A. Schober Cecilia Carbonelli Lorenzo Servadei Robert Wille 86 0 0 19 Jun 2023
Unlocking Layer-wise Relevance Propagation for Autoencoders Kenyu Kobayashi Renata Khasanova Arno Schneuwly Felix Schmidt Matteo Casserini FAtt 81 0 0 21 Mar 2023
Interpretable Ensembles of Hyper-Rectangles as Base Models A. Konstantinov Lev V. Utkin 133 3 0 15 Mar 2023
Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review C. Mendes T. N. Rios 63 11 0 27 Feb 2023
A Survey on Explainable Anomaly Detection Zhong Li Yuxuan Zhu M. Leeuwen 160 100 0 13 Oct 2022
Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations He Cheng Depeng Xu Shuhan Yuan Xintao Wu AI4TS 95 3 0 09 Oct 2022
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data Timur Sattarov Dayananda Herurkar Jörn Hees 103 9 0 21 Sep 2022
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations Ricardo Müller Marco Schreyer Timur Sattarov Damian Borth AAML MLAU 163 8 0 19 Sep 2022
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces Iñigo López-Riobóo Botana Carlos Eiras-Franco Julio César Hernández Castro Amparo Alonso-Betanzos 210 0 0 09 Sep 2022
A general-purpose method for applying Explainable AI for Anomaly Detection John Sipple Abdou Youssef 127 18 0 23 Jul 2022
Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities Subash Neupane Jesse Ables William Anderson Sudip Mittal Shahram Rahimi I. Banicescu Maria Seale AAML 142 88 0 13 Jul 2022
Towards Responsible AI for Financial Transactions Charl Maree Jan Erik Modal C. Omlin AAML 135 19 0 06 Jun 2022
PIXAL: Anomaly Reasoning with Visual Analytics Brian Montambault C. Brumar M. Behrisch Remco Chang 162 3 0 23 May 2022
Trustworthy Anomaly Detection: A Survey Shuhan Yuan Xintao Wu FaML 169 9 0 15 Feb 2022
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP) Khushnaseeb Roshan Aasim Zafar AAML 82 61 0 14 Dec 2021
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning Bang Xiang Yong Alexandra Brintrup 105 9 0 19 Oct 2021
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications Dongqi Han Zhiliang Wang Wenqi Chen Ying Zhong Su Wang Han Zhang Jiahai Yang Xingang Shi Xia Yin AAML 100 91 0 23 Sep 2021
An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data Lev V. Utkin A. Konstantinov Kirill Vishniakov FAtt 140 7 0 16 Jun 2021
Explainable Machine Learning for Fraud Detection I. Psychoula A. Gutmann Pradip Mainali Sharon H. Lee Paul Dunphy F. Petitcolas FaML 162 37 0 13 May 2021
Interpretation of multi-label classification models using shapley values Shikun Chen FAtt TDI 116 11 0 21 Apr 2021
A new interpretable unsupervised anomaly detection method based on residual explanation David F. N. Oliveira L. Vismari A. M. Nascimento J. R. de Almeida P. Cugnasca J. Camargo L. Almeida Rafael Gripp Marcelo M. Neves AAML 144 20 0 14 Mar 2021
Ensembles of Random SHAPs Lev V. Utkin A. Konstantinov FAtt 80 21 0 04 Mar 2021
Does the dataset meet your expectations? Explaining sample representation in image data Dhasarathy Parthasarathy Anton Johansson 55 0 0 06 Dec 2020
On the Nature and Types of Anomalies: A Review of Deviations in Data Ralph Foorthuis 257 97 0 30 Jul 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 262 637 0 16 Jun 2020
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection Naoya Takeishi FAtt 187 35 0 08 Sep 2019
VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks Sefi Akerman Edan Habler A. Shabtai 113 19 0 19 Jun 2019