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通过结合改进的变压器和CNN来检测恶意DNS.

Heyu Li1, Zhangmeizhi Li2, Shuyan Zhang3

  • 1Admission Office Changchun Sci-Tech University, Changchun, 130600, China. lhy18844033000@sina.com.

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|December 5, 2024
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概括

本研究介绍了一种改进的变压器和卷积神经网络模型,用于检测恶意域服务器. 与传统方法相比,这种新的方法显著提高了准确性和检测速度.

关键词:
在美国,CNN是CNN.恶意DNS的检测 恶意DNS的检测多重注意力机制多重注意力机制网络安全 网络安全变压器变压器变压器

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 随着互联网的广泛使用,网络安全威胁正在不断升级.
  • 域名服务器是关键基础设施,经常成为攻击的目标.
  • 传统的检测方法由于人工努力和静态规则而难以应对不断变化的威胁.

研究的目的:

  • 开发一种更具适应性和高效的方法来检测恶意域名服务器.
  • 克服传统基于规则和特征工程方法的局限性.
  • 为了提高恶意域名服务器识别的准确性和速度.

主要方法:

  • 改进了变压器模型,调整了注意力头和编码.
  • 增强型变压器与卷积神经网络的集成.
  • 在最终检测中使用基于块的集成分类器.

主要成果:

  • 获得了95.8%的平均准确度得分.
  • 显示了96.8%的平均检测时间得分.
  • 显示了96.3%的平均特征提取能力得分,整体性能为97.6%.

结论:

  • 拟议的方法在准确性和检测时间方面明显优于传统方法.
  • 这种新的技术为识别恶意域名服务器提供了一个强大的新工具.
  • 这些发现有助于推进对复杂威胁的网络安全防御.