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

• URL: http://arxiv.org/abs/2604.18052v1
• Date: Mon, 20 Apr 2026 10:19:57 GMT
• Title: ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
• Abstract summary: Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic.<n>We propose ExAI5G, a framework that prioritizes interpretability by integrating a Transformer-based deep learning IDS with logic-based explainable AI (XAI) techniques.<n>On a 5G IoT intrusion dataset, our system achieves 99.9% accuracy and a 0.854 macro F1-score, demonstrating strong performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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