Scholarlink

<p>The paper introduces a Universal Serial Bus (USB)-based defense framework, USB-GATE, which leverages a Generative Adversarial Network (GAN) and transformer-based embeddings to enhance the detection of adversarial keystroke injection attacks. USB-GATE uses a Wasserstein GAN with Gradient Penalty (WGAN-GP) to augment benign data. The framework combines benign data augmentation with multimodal transformer-based embeddings to improve the robustness of existing supervised Machine Learning (ML) models in detecting the attacks. The framework generates augmented benign data using WGAN-GP, establishing a robust baseline dataset. Subsequently, it leverages the Vision Transformer (ViT) component of Contrastive Language-Image Pre-training (CLIP) to generate embeddings that boost the performance of various supervised ML models in detecting attacks. Our evaluation highlights significant performance improvements, with the supervised ML model k-Nearest Neighbors (kNN) showing the maximum improvement, achieving a 17% boost in accuracy when the framework is applied. The Random Forest (RF) model achieves the best overall accuracy of 81.3%, marking a 5% improvement when using USB-GATE. Our results demonstrate the efficacy of USB-GATE in detecting adversarial attacks. This framework provides a promising solution for strengthening defenses. It is particularly effective against adversarial USB keystroke injection attacks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

USB-GATE: USB-based GAN-augmented transformer reinforced defense framework for adversarial keystroke injection attacks

  • 期刊论文

  • 发表日期：

    2025年2月16日

查看全文
  • Anil Kumar Chillara,
  • Paresh Saxena,
  • Rajib Ranjan Maiti

摘要

The paper introduces a Universal Serial Bus (USB)-based defense framework, USB-GATE, which leverages a Generative Adversarial Network (GAN) and transformer-based embeddings to enhance the detection of adversarial keystroke injection attacks. USB-GATE uses a Wasserstein GAN with Gradient Penalty (WGAN-GP) to augment benign data. The framework combines benign data augmentation with multimodal transformer-based embeddings to improve the robustness of existing supervised Machine Learning (ML) models in detecting the attacks. The framework generates augmented benign data using WGAN-GP, establishing a robust baseline dataset. Subsequently, it leverages the Vision Transformer (ViT) component of Contrastive Language-Image Pre-training (CLIP) to generate embeddings that boost the performance of various supervised ML models in detecting attacks. Our evaluation highlights significant performance improvements, with the supervised ML model k-Nearest Neighbors (kNN) showing the maximum improvement, achieving a 17% boost in accuracy when the framework is applied. The Random Forest (RF) model achieves the best overall accuracy of 81.3%, marking a 5% improvement when using USB-GATE. Our results demonstrate the efficacy of USB-GATE in detecting adversarial attacks. This framework provides a promising solution for strengthening defenses. It is particularly effective against adversarial USB keystroke injection attacks.