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

arXiv: https://arxiv.org/abs/2510.25810 · HTML: https://mlsanity.com/paper/2510.25810/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection](https://mlsanity.com/paper/2604.02149/) (2026)

## Abstract (arXiv)

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 underlineadversarial underlinetraffic-generating approach (AdvTrafficfootnoteThe 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%.