mlsanityData to 7 Oct 2026

A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv 2303.12942 ↗First posted 7 Mar 2023cs.CR cs.AI cs.NIcited by 9

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Abstract (arXiv)

The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of network cybersecurity, XAI has the potential to revolutionize the way we approach network security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity in network systems and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of network-driven cybersecurity threats and issues. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 31 of its 113 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

31 of 113 references.

References · 31

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  4. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  5. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  6. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
  7. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 205
  8. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  9. 2018Model Reconstruction from Model Explanationscited by 84
  10. 2019explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learningcited by 69
  11. 2020One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparencycited by 64
  12. 2020RelEx: A Model-Agnostic Relational Model Explainercited by 53
19 more
  1. 2019Interpretable and Differentially Private Predictionscited by 28
  2. 2020An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagerycited by 25
  3. 2022Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Researchcited by 24
  4. 2018An Adversarial Approach for Explainable AI in Intrusion Detection Systemscited by 22
  5. 2020Post-hoc explanation of black-box classifiers using confident itemsetscited by 21
  6. 2020xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systemscited by 18
  7. 2021Model Explainability in Deep Learning Based Natural Language Processingcited by 14
  8. 2020Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importancecited by 12
  9. 2021Explainable Artificial Intelligence for Human Decision-Support System in Medical Domaincited by 12
  10. 2022XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directionscited by 10
  11. 2022TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Securitycited by 9
  12. 2022Privacy Meets Explainability: A Comprehensive Impact Benchmarkcited by 9
  13. 2020Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Modelscited by 8
  14. 2019On the Privacy Risks of Model Explanationscited by 7
  15. 2022Anomaly Attribution with Likelihood Compensationcited by 7
  16. 2021Focused LRP: Explainable AI for Face Morphing Attack Detectioncited by 6
  17. 2018Explainable Black-Box Attacks Against Model-based Authenticationcited by 5
  18. 2020Multicollinearity Correction and Combined Feature Effect in Shapley Valuescited by 3
  19. 2021Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behaviorcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 0; 2026: 4.

9 papers.

  1. 2024From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Modelscited by 7
  2. 2024Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodologycited by 4
  3. 2024A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detectioncited by 2
  4. 2024XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensicscited by 1
  5. 2026Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Datacited by 1
  6. 2024Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Managementcited by 0
  7. 2026Cyber Threat Intelligence for Artificial Intelligence Systemscited by 0
  8. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Surveycited by 0
  9. 2026Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networkscited by 0