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Updated: Sep 17, 2025

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由人工智能驱动的网络安全:使用基于注意力的深度学习模型和优化算法来增强恶意域的检测.

Fatimah Alhayan1, Asma Alshuhail2, Ahmed Omer Ahmed Ismail3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific reports
|July 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种人工智能驱动的方法,EMDD-ADLMOA,通过分析其行为来检测恶意域. 该模型实现了98.52%的准确性,显著改善了针对在线威胁的网络安全防御.

关键词:
人工智能的人工智能是人工智能.网络安全 网络安全深度学习是一种深度学习.功能选择 功能选择恶意域名检测 恶意域名检测

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科学领域:

  • 网络安全 网络安全
  • 人工智能的人工智能
  • 网络安全 网络安全

背景情况:

  • 恶意域名对于网络攻击至关重要.
  • 域名系统 (DNS) 对互联网的功能至关重要.
  • 检测恶意域名对于网络安全至关重要.

研究的目的:

  • 提出一种用于检测恶意域名的增强技术.
  • 为了提高网络安全,利用人工智能.
  • 开发一个强大的和可扩展的恶意软件检测系统.

主要方法:

  • 使用min-max缩放进行预处理数据.
  • 使用量子启发的火虫算法 (QIFA) 进行特征选择.
  • 使用混合时空卷积网络 (TCN) 和双向长期短期记忆 (BiLSTM) 与挤压和刺激注意力 (SEA) 的混合时空卷积网络进行分类.
  • 使用Parrot优化 (PO) 算法进行超参数调整.

主要成果:

  • 拟议的EMDD-ADLMOA技术表现出卓越的性能.
  • 在检测恶意域名方面取得了98.52%的准确性.
  • 在实验验证方面表现优于现有方法.

结论:

  • 在EMDD-ADLMOA技术有效地提高恶意域的检测.
  • 基于人工智能的模型为强大的网络安全提供了一个有希望的方法.
  • 该研究为识别和减轻在线威胁提供了可扩展和有效的解决方案.