# XAI-based Feature Selection for Improved Network Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2410.10050 · HTML: https://mlsanity.com/paper/2410.10050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 104 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [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)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods](https://mlsanity.com/paper/1911.02508/) (2019)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Evaluating Explanation Methods for Deep Learning in Security](https://mlsanity.com/paper/1906.02108/) (2019)
- [Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2207.06236/) (2022)
- [Xplique: A Deep Learning Explainability Toolbox](https://mlsanity.com/paper/2206.04394/) (2022)
- [DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications](https://mlsanity.com/paper/2109.11495/) (2021)
- [Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response](https://mlsanity.com/paper/1911.09853/) (2019)
- [Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks](https://mlsanity.com/paper/2107.05172/) (2021)
- [Explainable AI for Comparative Analysis of Intrusion Detection Models](https://mlsanity.com/paper/2406.09684/) (2024)
- [Feature selection for intrusion detection systems](https://mlsanity.com/paper/2106.14941/) (2021)

## Abstract (arXiv)

Explainability and evaluation of AI models are crucial parts of the security of modern intrusion detection systems (IDS) in the network security field, yet they are lacking. Accordingly, feature selection is essential for such parts in IDS because it identifies the most paramount features, enhancing attack detection and its description. In this work, we tackle the feature selection problem for IDS by suggesting new ways of applying eXplainable AI (XAI) methods for this problem. We identify the crucial attributes originated by distinct AI methods in tandem with the novel five attribute selection methods. We then compare many state-of-the-art feature selection strategies with our XAI-based feature selection methods, showing that most AI models perform better when using the XAI-based approach proposed in this work. By providing novel feature selection techniques and establishing the foundation for several XAI-based strategies, this research aids security analysts in the AI decision-making reasoning of IDS by providing them with a better grasp of critical intrusion traits. Furthermore, we make the source codes available so that the community may develop additional models on top of our foundational XAI-based feature selection framework.