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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
概括
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.
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.
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