Beyond Measurement Metrics: A Human-Centered Framework for Semantic Validation of Network Traffic Classification

2026-09-15

AI Summary

本文针对网络流量分类模型的语义验证问题，提出结合数据、机器学习模型、可解释性、可视化和专家推理的人类中心框架，以确保模型准确且可信。

Abstract

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.

Problem

• network traffic classification
• semantic validation
• machine learning

Innovation

• human-centered framework
• semantic validation
• explainability
• visualization
• expert reasoning