# L-XAIDS: A LIME-based eXplainable AI framework for Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2508.17244 · HTML: https://mlsanity.com/paper/2508.17244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 83 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Transparency and Explanation in Deep Reinforcement Learning Neural Networks](https://mlsanity.com/paper/1809.06061/) (2018)
- [Communication-Efficient Distributed Dual Coordinate Ascent](https://mlsanity.com/paper/1409.1458/) (2014)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability](https://mlsanity.com/paper/2109.05327/) (2021)
- [Rallying Adversarial Techniques against Deep Learning for Network Security](https://mlsanity.com/paper/1903.11688/) (2019)
- [A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models](https://mlsanity.com/paper/1812.09059/) (2018)
- [A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data](https://mlsanity.com/paper/1709.03082/) (2017)
- [Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection](https://mlsanity.com/paper/2104.07183/) (2021)
- [Explanations of Black-Box Model Predictions by Contextual Importance and Utility](https://mlsanity.com/paper/2006.00199/) (2020)
- [Internet of Things Security and Forensics: Challenges and Opportunities](https://mlsanity.com/paper/1807.10438/) (2018)
- [SCADS: A Scalable Approach Using Spark in Cloud for Host-based Intrusion Detection System with System Calls](https://mlsanity.com/paper/2109.11821/) (2021)
- [AI security and cyber risk in IoT systems](https://mlsanity.com/paper/2410.09194/) (2024)

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

- [CyTReX: Explainable AI-Based Cybersecurity Threat Reasoning Framework for DER Networks](https://mlsanity.com/paper/2610.04286/) (2026)

## Abstract (arXiv)

Recent developments in Artificial Intelligence (AI) and their applications in critical industries such as healthcare, fin-tech and cybersecurity have led to a surge in research in explainability in AI. Innovative research methods are being explored to extract meaningful insight from blackbox AI systems to make the decision-making technology transparent and interpretable. Explainability becomes all the more critical when AI is used in decision making in domains like fintech, healthcare and safety critical systems such as cybersecurity and autonomous vehicles. However, there is still ambiguity lingering on the reliable evaluations for the users and nature of transparency in the explanations provided for the decisions made by black-boxed AI. To solve the blackbox nature of Machine Learning based Intrusion Detection Systems, a framework is proposed in this paper to give an explanation for IDSs decision making. This framework uses Local Interpretable Model-Agnostic Explanations (LIME) coupled with Explain Like I'm five (ELI5) and Decision Tree algorithms to provide local and global explanations and improve the interpretation of IDSs. The local explanations provide the justification for the decision made on a specific input. Whereas, the global explanations provides the list of significant features and their relationship with attack traffic. In addition, this framework brings transparency in the field of ML driven IDS that might be highly significant for wide scale adoption of eXplainable AI in cyber-critical systems. Our framework is able to achieve 85 percent accuracy in classifying attack behaviour on UNSW-NB15 dataset, while at the same time displaying the feature significance ranking of the top 10 features used in the classification.