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

TL;DR

This paper introduces L-XAIDS, an explainable AI framework for intrusion detection systems that combines LIME, ELI5, and decision trees to provide both local and global explanations, enhancing transparency and interpretability.

Contribution

The novel framework integrates LIME, ELI5, and decision trees to improve interpretability of ML-based IDSs, offering local and global explanations for decision transparency.

Findings

Achieves 85% accuracy on UNSW-NB15 dataset.

Provides feature importance rankings for attack classification.

Enhances transparency in ML-driven intrusion detection.

Abstract

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.