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科学领域:

  • 网络安全 网络安全
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 勒索软件电子邮件网络鱼 (REP) 攻击构成重大威胁,利用欺骗性的电子邮件和短信.
  • 现有的检测方法往往忽略了语言细微差别,如电子邮件文本中的形和语言包.
  • 目前的模型缺乏分析新型网络鱼策略中使用的特定文本特征的复杂性.

研究的目的:

  • 提出一种基于语言包的新调整型变压器语言 (LPTTL) 框架,用于增强勒索软件电子邮件鱼 (REP) 攻击的检测.
  • 在识别网络鱼尝试时,引入先进的形算法来进行细微的文本分析.
  • 根据代币化的电子邮件文本开发强大的分类模型,以准确地对REP威胁进行分类.

主要方法:

  • 在LPTTL框架中,用于检测REP攻击,使用了各种编码算法,包括Language Pack Tuned双向编码器表示转换器 (LPT-BERT) 和文本转移转换器 (LPT-T5).
  • 电子邮件体文本被令牌化以分析单词和嵌入的语言模式.
  • 分类采用诸如鸟优化算法-长期短期记忆 (LOA-LSTM),河马优化 (HO) 门式循环神经网络 (HO-GRU) 和Meerkat优化算法-双向长期短期记忆 (MOA-BiLSTM) 等算法.

主要成果:

  • 拟议的LPTTL框架显示了大约95.47%的高精度.
  • 该系统的精度为96.8%,回忆率为95.63%,F1得分为96.21%.
  • 这些性能指标在检测勒索软件电子邮件网络鱼方面显著优于现有的方法.

结论:

  • LPTTL框架在打击新的勒索软件电子邮件网络鱼攻击方面取得了重大进展.
  • 混凝土分析和先进的变压器模型的整合提供了一个更有效的检测机制.
  • 该研究强调了语言包特定分析的潜力,以改善针对复杂的网络鱼的网络安全防御.