ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks

2026-04-20

AI Summary

This study addresses the challenge of poor model interpretability and opaque decision-making in 5G network intrusion detection caused by high-dimensional traffic data. To this end, it proposes the first explainable AI (XAI) framework that integrates logical rule extraction with large language model (LLM)-driven evaluation. The approach combines a Transformer-based detection model, Integrated Gradients for feature attribution, and a surrogate decision tree to generate human-interpretable logical rules. These rules are then evaluated by an LLM for fidelity and actionability. Evaluated on a 5G IoT dataset, the system achieves 99.9% accuracy and a macro F1-score of 0.854, while extracting 16 high-fidelity logical rules (99.7% fidelity), thereby unifying high detection performance with strong interpretability and introducing a novel mechanism for assessing explanation quality.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque "black-box" models can achieve high accuracy, their lack of transparency hinders trust and effective operational response. We propose ExAI5G, a framework that prioritizes interpretability by integrating a Transformer-based deep learning IDS with logic-based explainable AI (XAI) techniques. The framework uses Integrated Gradients to attribute feature importance and extracts a surrogate decision tree to derive logical rules. We introduce a novel evaluation methodology for LLM-generated explanations, using a powerful evaluator LLM to assess actionability and measuring their semantic similarity and faithfulness. On a 5G IoT intrusion dataset, our system achieves 99.9\% accuracy and a 0.854 macro F1-score, demonstrating strong performance. More importantly, we extract 16 logical rules with 99.7\% fidelity, making the model's reasoning transparent. The evaluation demonstrates that modern LLMs can generate explanations that are both faithful and actionable, indicating that it is possible to build a trustworthy and effective IDS without compromising performance for the sake of marginal gains from an opaque model.

Problem

• Intrusion Detection
• 5G Networks
• Explainable AI
• Black-box Models
• Model Transparency

Innovation

• Explainable AI (XAI)
• Transformer-based IDS
• Logical rule extraction
• Integrated Gradients
• LLM-based explanation evaluation