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相关实验视频

使用现代BERT进行可解释的少量学习,用于检测使用XF PhishBERT的新兴网络鱼攻击.

Mohammed Tawfik1, Ashraf A Abu-Ein2,3, Amr H Abdelhaliem4

  • 1Faculty of Computer and Information Technology, Sana'a University, Sana'a, Yemen. kmkhol01@gmail.com.

Scientific reports
|December 1, 2025
PubMed
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XF-PhishBERT为网络鱼检测提供可解释的几次学习,使用最小的数据实现高精度. 这种网络安全解决方案通过有效地适应新威胁,克服了传统方法的局限性.

科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 网络鱼攻击迅速发展,超过了传统的检测系统.
  • 机器学习模型需要大量的标记数据,为新威胁创造漏洞.
  • 为了应对新出现的网络威胁,获取标记数据是昂贵且耗时的.

研究的目的:

  • 介绍XF-PhishBERT,这是一个可解释的几次射击学习框架,用于有效检测网络鱼.
  • 以最少的培训示例来实现有效的网络鱼检测.
  • 为安全分析师提供透明的决策支持.

主要方法:

  • 结合了ModernBERT变压器架构与域特定的URL特性.
  • 集成的原型网络和模型无意识的超级学习 (MAML) 为少数人学习.
  • 使用基于共识的特征选择 (随机森林,相互信息,RFECV) 和SHAP分析来解释.

主要成果:

  • 通过每班10个示例实现了99.9%的准确性,在一次性学习中达到98.5%.
  • 在交叉数据集评估中证明了186%的性能保留,显著超过传统方法 (39%).
  • 浏览器扩展部署显示了98.3%的精度和42ms的延迟.
关键词:
贝尔特 (BERT) 公司有几次射击学习学习.大型语言模型 (LLM)超级学习 (Meta-learning) 是一种学习方式.网络鱼检测 网络鱼检测

相关实验视频

结论:

  • 短暂的学习有效地解决了网络安全中有限的标记数据的挑战.
  • XF-PhishBERT为快速发展的网络鱼威胁提供了强大且易于解释的解决方案.
  • 该框架通过快速适应和透明的威胁分析来加强网络安全.