一种用于语义验证网络流量分类的人本框架
Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification
这篇论文讲了一个很实用的框架，能帮你更深入地验证AI模型在分类网络流量时的效果，不是只看准确率，而是看它学到的模式是不是有意义的。
本文提出了一种结合数据、机器学习模型、可解释性、可视化和专家推理的框架，用于迭代探索、验证和优化模型行为及数据预处理。该框架基于文献发现、基准数据集分析、XAI基础上的网络流量分类实践经验以及专家反馈，为语义模型验证提供实用指导。通过补充预测性能与语义验证和人类专业知识，该框架支持开发不仅准确，而且鲁棒和值得信赖的网络流量分类模型。
Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification
Machine learning (ML) has become the dominant approach for network traffic classification, achieving very high predictive performance. However, a model is only valuable if it learns semantically meaningful and trustworthy patterns rather than exploiting spurious correlations. Conventional evaluation practices predominantly assess predictive performance. Consequently, whether the model relies on semantically meaningful patterns remains unknown. To address these challenges, we adapt the knowledge generation framework for network traffic classification. The adapted framework combines data, ML models, explainability, visualization, and expert reasoning to support the iterative exploration, verification, and refinement of model behavior and data preprocessing. The framework is grounded in findings from the literature, benchmark dataset analyses, practical experience with XAI-based traffic classification, and expert feedback, providing practical guidance for semantic model validation. By complementing predictive performance with semantic validation and human expertise, the proposed framework supports the development of network traffic classification models that are not only accurate but also robust and trustworthy.