BDpackets: A Clean-label Backdoor Attack on Network Traffic Classifiers via Feature Fusion

Machine learning-based network traffic classification models are vulnerable to backdoor attacks. Recent work demonstrates that packet-level backdoor attacks are feasible even in black-box settings, but their attack success rates remain low under clean-label conditions (e.g., 36.26% on ISCX VPN-nonVPN). Unlike image-based models, simple trigger patterns are ineffective against traffic classifiers because of the unique characteristics of network data. These include variable-length packet sequences, strict protocol compliance, and semantic constraints that limit allowable modifications. Such challenges make it difficult for fixed-pattern triggers to maintain integrity, saliency, and consistency across diverse network environments. Motivated by this insight, we propose BDpackets, a packet-level clean-label backdoor attack that employs a novel feature fusion technique to nonlinearly integrate salient features and generate semantically rich triggers, thereby significantly improving attack effectiveness. Experiments on multiple datasets and model architectures show that BDpackets achieves an attack success rate of 98.91%, outperforming prior backdoor methods by 62.65% and 45.36% in absolute terms. Moreover, BDpackets maintains stealthiness and successfully evades detection by state-of-the-art defenses.