Bias in the Shadows: Explore Shortcuts in Encrypted Network Traffic Classification

• URL: http://arxiv.org/abs/2601.10180v1
• Date: Thu, 15 Jan 2026 08:39:56 GMT
• Title: Bias in the Shadows: Explore Shortcuts in Encrypted Network Traffic Classification
• Authors: Chuyi Wang, Xiaohui Xie, Tongze Wang, Yong Cui,
• Abstract summary: BiasSeeker is a semi-automated framework for detecting dataset-specific shortcut features in encrypted traffic.<n>We introduce a systematic categorization and apply category-specific validation strategies that reduce bias while preserving meaningful information.<n>We evaluate BiasSeeker on 19 public datasets across three NTC tasks.
• Score: 8.740413164300957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained models operating directly on raw bytes have achieved promising performance in encrypted network traffic classification (NTC), but often suffer from shortcut learning-relying on spurious correlations that fail to generalize to real-world data. Existing solutions heavily rely on model-specific interpretation techniques, which lack adaptability and generality across different model architectures and deployment scenarios. In this paper, we propose BiasSeeker, the first semi-automated framework that is both model-agnostic and data-driven for detecting dataset-specific shortcut features in encrypted traffic. By performing statistical correlation analysis directly on raw binary traffic, BiasSeeker identifies spurious or environment-entangled features that may compromise generalization, independent of any classifier. To address the diverse nature of shortcut features, we introduce a systematic categorization and apply category-specific validation strategies that reduce bias while preserving meaningful information. We evaluate BiasSeeker on 19 public datasets across three NTC tasks. By emphasizing context-aware feature selection and dataset-specific diagnosis, BiasSeeker offers a novel perspective for understanding and addressing shortcut learning in encrypted network traffic classification, raising awareness that feature selection should be an intentional and scenario-sensitive step prior to model training.

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