# XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions

arXiv: https://arxiv.org/abs/2206.03585 · HTML: https://mlsanity.com/paper/2206.03585/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 107 references that are in the map.
- First posted on arXiv 3 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks](https://mlsanity.com/paper/2105.03592/) (2021)
- [Reviewing the Need for Explainable Artificial Intelligence (xAI)](https://mlsanity.com/paper/2012.01007/) (2020)
- [DALEX: explainers for complex predictive models](https://mlsanity.com/paper/1806.08915/) (2018)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [SOK: A Comprehensive Reexamination of Phishing Research from the Security Perspective](https://mlsanity.com/paper/1911.00953/) (2019)
- [Explaining Network Intrusion Detection System Using Explainable AI Framework](https://mlsanity.com/paper/2103.07110/) (2021)
- [Evaluation of Machine Learning Algorithms for Intrusion Detection System](https://mlsanity.com/paper/1801.02330/) (2018)
- [Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges](https://mlsanity.com/paper/2012.09110/) (2020)
- [TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security](https://mlsanity.com/paper/2205.01232/) (2022)
- [Zero-shot learning approach to adaptive Cybersecurity using Explainable AI](https://mlsanity.com/paper/2106.14647/) (2021)
- [Blockchain based AI-enabled Industry 4.0 CPS Protection against Advanced Persistent Threat](https://mlsanity.com/paper/2201.12727/) (2022)
- [A Secure Fog Based Architecture for Industrial Internet of Things and Industry 4.0](https://mlsanity.com/paper/2005.07147/) (2020)
- [Physical Layer Security Protocol for Poisson Channels for Passive Man-in-the-middle Attack](https://mlsanity.com/paper/1912.13120/) (2019)
- [A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems](https://mlsanity.com/paper/2103.03472/) (2021)

## Built on by (most cited first, 10 of 10)

- [Foiling Explanations in Deep Neural Networks](https://mlsanity.com/paper/2211.14860/) (2022)
- [Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions](https://mlsanity.com/paper/2209.04160/) (2022)
- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)
- [Self-Deception: Reverse Penetrating the Semantic Firewall of Large Language Models](https://mlsanity.com/paper/2308.11521/) (2023)
- [SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning](https://mlsanity.com/paper/2312.16191/) (2023)
- [Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models](https://mlsanity.com/paper/2406.04926/) (2024)
- [Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data](https://mlsanity.com/paper/2608.13575/) (2026)
- [A Systematic Literature Review of Soft Computing Techniques for Software Maintainability Prediction: State-of-the-Art, Challenges and Future Directions](https://mlsanity.com/paper/2209.10131/) (2022)
- [Tensions Between the Proxies of Human Values in AI](https://mlsanity.com/paper/2212.07508/) (2022)
- [Explainable Threat Attribution for IoT Networks Using Conditional SHAP and Flow Behavior Modelling](https://mlsanity.com/paper/2603.22771/) (2026)

## Abstract (arXiv)

In the past few years, artificial intelligence (AI) techniques have been implemented in almost all verticals of human life. However, the results generated from the AI models often lag explainability. AI models often appear as a blackbox wherein developers are unable to explain or trace back the reasoning behind a specific decision. Explainable AI (XAI) is a rapid growing field of research which helps to extract information and also visualize the results generated with an optimum transparency. The present study provides and extensive review of the use of XAI in cybersecurity. Cybersecurity enables protection of systems, networks and programs from different types of attacks. The use of XAI has immense potential in predicting such attacks. The paper provides a brief overview on cybersecurity and the various forms of attack. Then the use of traditional AI techniques and its associated challenges are discussed which opens its doors towards use of XAI in various applications. The XAI implementations of various research projects and industry are also presented. Finally, the lessons learnt from these applications are highlighted which act as a guide for future scope of research.