# Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

arXiv: https://arxiv.org/abs/2208.14937 · HTML: https://mlsanity.com/paper/2208.14937/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2022.

## Built on by (most cited first, 25 of 25)

- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)
- [A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence](https://mlsanity.com/paper/2311.01154/) (2023)
- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis](https://mlsanity.com/paper/2502.21286/) (2025)
- [Probabilistic Lipschitzness and the Stable Rank for Comparing Explanation Models](https://mlsanity.com/paper/2402.18863/) (2024)
- [Algebraic Adversarial Attacks on Integrated Gradients](https://mlsanity.com/paper/2407.16233/) (2024)
- [Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals](https://mlsanity.com/paper/2301.06923/) (2023)
- [Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence](https://mlsanity.com/paper/2402.07915/) (2024)
- [New data poison attacks on machine learning classifiers for mobile exfiltration](https://mlsanity.com/paper/2210.11592/) (2022)
- [Scalable Concept Extraction in Industry 4.0](https://mlsanity.com/paper/2306.03551/) (2023)
- [A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail](https://mlsanity.com/paper/2210.14616/) (2022)
- [XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics](https://mlsanity.com/paper/2402.02452/) (2024)
- [Enhancing Reasoning Capacity of SLM using Cognitive Enhancement](https://mlsanity.com/paper/2404.01135/) (2024)
- [CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection](https://mlsanity.com/paper/2503.00961/) (2025)
- [Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods](https://mlsanity.com/paper/2505.10050/) (2025)
- [Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks](https://mlsanity.com/paper/2609.12305/) (2026)
- [Explainable Label-flipping Attacks on Human Emotion Assessment System](https://mlsanity.com/paper/2302.04109/) (2023)
- [Easydiagnos: a framework for accurate feature selection for automatic diagnosis in smart healthcare](https://mlsanity.com/paper/2410.00366/) (2024)
- [Algebraic Adversarial Attacks on Explainability Models](https://mlsanity.com/paper/2503.12683/) (2025)
- [An Explainable Multi-Task Similarity Measure: Integrating Accumulated Local Effects and Weighted Fr'echet Distance](https://mlsanity.com/paper/2602.07966/) (2026)
- [Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy](https://mlsanity.com/paper/2602.10100/) (2026)
- [Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection](https://mlsanity.com/paper/2605.09028/) (2026)
- [Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018](https://mlsanity.com/paper/2606.05714/) (2026)
- [From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond](https://mlsanity.com/paper/2607.05563/) (2026)
- [(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations](https://mlsanity.com/paper/2607.26201/) (2026)

## Abstract (arXiv)

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the black-box manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparency and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security.