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

2026-01-15

AI Summary

This work addresses the poor generalization of encrypted network traffic classification models, which often stems from reliance on spurious correlations—commonly known as shortcut learning. The authors propose BiasSeeker, a model-agnostic, data-driven framework for systematically identifying shortcut features. By conducting statistical correlation analyses on raw byte-level traffic, BiasSeeker constructs a structured taxonomy of shortcut features and introduces a context-aware validation mechanism to verify their impact. Extensive experiments across 19 public datasets and three encrypted traffic classification tasks demonstrate that the framework significantly enhances model generalization in real-world scenarios. The findings underscore the critical role of context-sensitive and intentional feature selection in mitigating shortcut learning and improving robustness.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• shortcut learning
• encrypted network traffic classification
• spurious correlations
• dataset bias
• model generalization

Innovation

• shortcut learning
• encrypted traffic classification
• model-agnostic
• bias detection
• feature selection