# A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv: https://arxiv.org/abs/2303.12942 · HTML: https://mlsanity.com/paper/2303.12942/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 113 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 30 of 31)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Explaining individual predictions when features are dependent: More accurate approximations to Shapley values](https://mlsanity.com/paper/1903.10464/) (2019)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation](https://mlsanity.com/paper/2206.03208/) (2022)
- [Model Reconstruction from Model Explanations](https://mlsanity.com/paper/1807.05185/) (2018)
- [explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning](https://mlsanity.com/paper/1908.00087/) (2019)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)
- [RelEx: A Model-Agnostic Relational Model Explainer](https://mlsanity.com/paper/2006.00305/) (2020)
- [Interpretable and Differentially Private Predictions](https://mlsanity.com/paper/1906.02004/) (2019)
- [An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTscan Imagery](https://mlsanity.com/paper/2008.00238/) (2020)
- [Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research](https://mlsanity.com/paper/2208.14937/) (2022)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [Post-hoc explanation of black-box classifiers using confident itemsets](https://mlsanity.com/paper/2005.01992/) (2020)
- [xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems](https://mlsanity.com/paper/2002.10438/) (2020)
- [Model Explainability in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/2106.07410/) (2021)
- [Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance](https://mlsanity.com/paper/2007.11117/) (2020)
- [Explainable Artificial Intelligence for Human Decision-Support System in Medical Domain](https://mlsanity.com/paper/2105.02357/) (2021)
- [XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions](https://mlsanity.com/paper/2206.03585/) (2022)
- [TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security](https://mlsanity.com/paper/2205.01232/) (2022)
- [Privacy Meets Explainability: A Comprehensive Impact Benchmark](https://mlsanity.com/paper/2211.04110/) (2022)
- [Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models](https://mlsanity.com/paper/2001.01037/) (2020)
- [On the Privacy Risks of Model Explanations](https://mlsanity.com/paper/1907.00164/) (2019)
- [Anomaly Attribution with Likelihood Compensation](https://mlsanity.com/paper/2208.10679/) (2022)
- [Focused LRP: Explainable AI for Face Morphing Attack Detection](https://mlsanity.com/paper/2103.14697/) (2021)
- [Explainable Black-Box Attacks Against Model-based Authentication](https://mlsanity.com/paper/1810.00024/) (2018)
- [Multicollinearity Correction and Combined Feature Effect in Shapley Values](https://mlsanity.com/paper/2011.01661/) (2020)

## Built on by (most cited first, 9 of 9)

- [From COBIT to ISO 42001: Evaluating Cybersecurity Frameworks for Opportunities, Risks, and Regulatory Compliance in Commercializing Large Language Models](https://mlsanity.com/paper/2402.15770/) (2024)
- [Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology](https://mlsanity.com/paper/2404.04205/) (2024)
- [A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection](https://mlsanity.com/paper/2407.04009/) (2024)
- [XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics](https://mlsanity.com/paper/2402.02452/) (2024)
- [Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data](https://mlsanity.com/paper/2608.13575/) (2026)
- [Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks](https://mlsanity.com/paper/2609.12305/) (2026)
- [Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management](https://mlsanity.com/paper/2402.15945/) (2024)
- [Cyber Threat Intelligence for Artificial Intelligence Systems](https://mlsanity.com/paper/2603.05068/) (2026)
- [Generative AI and Federated Learning for Intrusion Detection Systems: A Survey](https://mlsanity.com/paper/2607.01305/) (2026)

## 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.