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相关实验视频

通过深度学习和自适应优化来提高网络鱼电子邮件检测性能.

Mehdi Hosseinzadeh1,2,3, Usman Ali4, Saqib Ali5

  • 1Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. mehdihosseinzadeh@duytan.edu.vn.

Scientific reports
|October 21, 2025
PubMed
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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查看所有相关文章
此摘要是机器生成的。

这项研究引入了一种混合深度学习模型,使用来自变压器的双向编码器表示 (BERT) 和山地子优化器 (MGO) 来进行先进的网络鱼电子邮件检测,显著提高准确性并减少假阳性.

科学领域:

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

背景情况:

  • 网络鱼电子邮件攻击越来越复杂,挑战了传统的检测方法.
  • 区分合法的电子邮件和恶意电子邮件需要先进的技术,因为真实的假冒.
  • 现有的网络安全措施面临着对不断发展的网络鱼策略的限制.

研究的目的:

  • 提出一种新的混合深度学习架构,用于增强网络鱼电子邮件检测.
  • 利用来自变压器的双向编码器表示 (BERT) 来进行上下文理解.
  • 为了优化模型使用山地鱼优化器 (MGO) 以提高性能.

主要方法:

  • 这是一个混合架构,结合了BERT嵌入,卷积神经网络 (CNN) 和门式循环单元 (GRU).
  • 整合多头注意力,以精细关注关键电子邮件功能.
  • 在Kaggle网络鱼电子邮件数据集上使用Mountain Gazelle Optimizer (MGO) 的超参数优化.

主要成果:

  • 实现了高分类准确度 (96.8%),精度 (97.2%),回忆 (95.4%) 和F1得分 (96.3%).
  • 与最先进的方法相比,虚假阳性结果减少了2.5%.
关键词:
深度学习是一种深度学习.电子邮件安全 电子邮件安全机器学习是机器学习.山的优化器 山的优化器优化优化 优化优化网络鱼电子邮件检测检测

相关实验视频

  • 验证了该模型对复杂的网络鱼威胁的有效性.

    • 结论:

    • 拟议的混合深度学习模型显著提高了网络鱼电子邮件检测能力.
    • 基于变压器的嵌入与先进的神经网络和优化相结合,有效抵御网络鱼.
    • 优化MGO架构为改善电子邮件安全提供了强大的解决方案.