{"ID":2848493,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25810","arxiv_id":"2510.25810","title":"Adversarial Pre-Padding: Generating Evasive Network Traffic Against Transformer-Based Classifiers","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: https://anonymous.4open.science/r/TrafficD-C461}). 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\\%.","short_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 witne...","url_abs":"https://arxiv.org/abs/2510.25810","url_pdf":"https://arxiv.org/pdf/2510.25810v2","authors":"[\"Quanliang Jing\",\"Xinxin Fan\",\"Yanyan Liu\",\"Jingping Bi\"]","published":"2025-10-29T09:45:27Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.NI\"]","methods":"[\"Reinforcement Learning\",\"Transformer\"]","project_urls":"[\"https://anonymous.4open.science/r/TrafficD-C461\"]","has_code":false}