Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review
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Abstract (arXiv)
Cybersecurity vendors consistently apply AI (Artificial Intelligence) to their solutions and many cybersecurity domains can benefit from AI technology. However, black-box AI techniques present some difficulties in comprehension and adoption by its operators, given that their decisions are not always humanly understandable (as is usually the case with deep neural networks, for example). Since it aims to make the operation of AI algorithms more interpretable for its users and developers, XAI (eXplainable Artificial Intelligence) can be used to address this issue. Through a systematic literature review, this work seeks to investigate the current research scenario on XAI applied to cybersecurity, aiming to discover which XAI techniques have been applied in cybersecurity, and which areas of cybersecurity have already benefited from this technology.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 52 references that are in the map. First posted on arXiv 27 Feb 2023.
Builds on
10 of 52 references.
References · 10
- 2019Explaining Anomalies Detected by Autoencoders Using SHAPcited by 27
- 2018An Adversarial Approach for Explainable AI in Intrusion Detection Systemscited by 22
- 2021Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)cited by 15
- 2021Explaining Network Intrusion Detection System Using Explainable AI Frameworkcited by 11
- 2019Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Responsecited by 10
- 2022TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Securitycited by 9
- 2021On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)cited by 7
- 2021Zero-shot learning approach to adaptive Cybersecurity using Explainable AIcited by 5
- 2020Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?cited by 4
- 2021First Step Towards EXPLAINable DGA Multiclass Classificationcited by 4
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Citing papers per year: 2023: 1.
1 paper.