# Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2303.01259 · HTML: https://mlsanity.com/paper/2303.01259/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Explaining Anomalies Detected by Autoencoders Using SHAP](https://mlsanity.com/paper/1903.02407/) (2019)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)](https://mlsanity.com/paper/2112.08442/) (2021)
- [Explaining Network Intrusion Detection System Using Explainable AI Framework](https://mlsanity.com/paper/2103.07110/) (2021)
- [Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response](https://mlsanity.com/paper/1911.09853/) (2019)
- [TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security](https://mlsanity.com/paper/2205.01232/) (2022)
- [On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)](https://mlsanity.com/paper/2108.02006/) (2021)
- [Zero-shot learning approach to adaptive Cybersecurity using Explainable AI](https://mlsanity.com/paper/2106.14647/) (2021)
- [Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?](https://mlsanity.com/paper/2005.01452/) (2020)
- [First Step Towards EXPLAINable DGA Multiclass Classification](https://mlsanity.com/paper/2106.12336/) (2021)

## Built on by (most cited first, 1 of 1)

- [IsoEx: an explainable unsupervised approach to process event logs cyber investigation](https://mlsanity.com/paper/2306.09260/) (2023)

## 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.