Adversarial Pre-Padding: Generating Evasive Network Traffic Against Transformer-Based Classifiers

• URL: http://arxiv.org/abs/2510.25810v1
• Date: Wed, 29 Oct 2025 09:45:27 GMT
• Title: Adversarial Pre-Padding: Generating Evasive Network Traffic Against Transformer-Based Classifiers
• Authors: Quanliang Jing, Xinxin Fan, Yanyan Liu, Jingping Bi,
• Abstract summary: We propose a novel and effective underlineadversarial underlinetraffic-generating approach.<n>Our approach has two key innovations: (i) a pre-padding strategy is proposed to modify packets, which effectively overcomes the limitations of existing research against transformer-based models for network traffic classification; and (ii) a reinforcement learning model is employed to optimize network traffic perturbations.
• Score: 7.283896930735145
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: To date, traffic obfuscation techniques have been widely adopted to protect network data privacy and security by obscuring the true patterns of traffic. Nevertheless, as the pre-trained models emerge, especially transformer-based classifiers, existing traffic obfuscation methods become increasingly vulnerable, as witnessed by current studies reporting the traffic classification accuracy up to 99\% or higher. To counter such high-performance transformer-based classification models, we in this paper propose a novel and effective \underline{adv}ersarial \underline{traffic}-generating approach (AdvTraffic\footnote{The code and data are available at: http://xxx}). Our approach has two key innovations: (i) a pre-padding strategy is proposed to modify packets, which effectively overcomes the limitations of existing research against transformer-based models for network traffic classification; and (ii) a reinforcement learning model is employed to optimize network traffic perturbations, aiming to maximize adversarial effectiveness against transformer-based classification models. To the best of our knowledge, this is the first attempt to apply adversarial perturbation techniques to defend against transformer-based traffic classifiers. Furthermore, our method can be easily deployed into practical network environments. Finally, multi-faceted experiments are conducted across several real-world datasets, and the experimental results demonstrate that our proposed method can effectively undermine transformer-based classifiers, significantly reducing classification accuracy from 99\% to as low as 25.68\%.

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