Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matters

• URL: http://arxiv.org/abs/2512.02276v1
• Date: Mon, 01 Dec 2025 23:47:22 GMT
• Title: Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matters
• Authors: Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino,
• Abstract summary: Traffic classification (TC) plays a critical role in cybersecurity, particularly in IoT and embedded contexts.<n>We use hardware-aware neural architecture search (HW-NAS) to derive lightweight TC models that are accurate, efficient, and deployable on edge platforms.
• Score: 2.4779082385578337
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traffic classification (TC) plays a critical role in cybersecurity, particularly in IoT and embedded contexts, where inspection must often occur locally under tight hardware constraints. We use hardware-aware neural architecture search (HW-NAS) to derive lightweight TC models that are accurate, efficient, and deployable on edge platforms. Two input formats are considered: a flattened byte sequence and a 2D packet-wise time series; we examine how input structure affects adversarial vulnerability when using resource-constrained models. Robustness is assessed against white-box attacks, specifically Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). On USTC-TFC2016, both HW-NAS models achieve over 99% clean-data accuracy while remaining within 65k parameters and 2M FLOPs. Yet under perturbations of strength 0.1, their robustness diverges: the flat model retains over 85% accuracy, while the time-series variant drops below 35%. Adversarial fine-tuning delivers robust gains, with flat-input accuracy exceeding 96% and the time-series variant recovering over 60 percentage points in robustness, all without compromising efficiency. The results underscore how input structure influences adversarial vulnerability, and show that even compact, resource-efficient models can attain strong robustness, supporting their practical deployment in secure edge-based TC.

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