用逻辑规则解释5G入侵检测模型，既准又透明。
ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
- 结合Transformer与逻辑规则提取，实现可解释的5G入侵检测
- 在5G IoT数据集上达99.9%准确率，宏F1为0.854
- 生成的16条规则忠实度99.7%，适合安全运维人员使用
针对5G网络高复杂度、高流量的入侵检测需求，传统黑箱模型虽精度高但缺乏透明性。本文提出ExAI5G框架，将基于Transformer的深度学习入侵检测系统与基于逻辑的可解释AI技术结合。利用集成梯度进行特征重要性归因，并通过替代决策树提取逻辑规则。提出一种新颖的LLM解释评估方法，使用强大评测LLM判断解释的可操作性，同时测量语义相似性与忠实度。在5G IoT入侵检测数据集上，系统达到99.9%准确率和0.854宏F1分数，表现优异。更重要的是，成功提取16条逻辑规则，忠实度达99.7%，使模型推理过程完全透明。评估表明，现代LLM能生成既忠实又可操作的解释，证明可在不牺牲性能的前提下构建可信高效的入侵检测系统。
原文摘要 · Abstract (English)
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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