arXiv Machine Learning September 16, 2026 By Igor Cherepanov, David Sessler, Alex Ulmer, Thorsten May, J\"orn Kohlhammer

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

Read the original on arXiv Machine Learning →

The paper proposes a human-centered framework for validating the semantic soundness of machine learning models used in network traffic classification. It extends existing knowledge-generation methods by integrating data, models, explainability tools, visualizations, and expert reasoning to iteratively explore, verify, and refine model behavior and preprocessing steps. The framework is built on literature findings, benchmark analyses, XAI experience, and expert feedback, offering practical guidance for ensuring models learn trustworthy, semantically meaningful patterns rather than spurious correlations.

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