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在使用基于启发式优化算法的网络物理系统中检测攻击的新兴框架.

Manal Abdullah Alohali1, Muna Elsadig1, Anwer Mustafa Hilal2

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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概括
此摘要是机器生成的。

这项研究引入了一个新的框架,深袋式卷积神经网络与启发式多群殖民地优化 (DCNN-HMACO),用于增强网络物理系统 (CPS) 的安全性. DCNN-HMACO框架显著提高了攻击检测率,确保了更安全的信息传输.

关键词:
殖民地优化殖民地优化在美国,CNN是CNN.网络物理系统 网络物理系统深层包装深层包装启发式 启发式是一种启发式的启发式.

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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:

  • 网络物理系统 (CPS) 越来越多地成为现代生活的组成部分,包括用于数据共享和远程访问的复杂网络.
  • 由于CPS的互联性,因此需要采取强有力的安全措施,以保护敏感信息和系统资源免受网络攻击.
  • 现有的研究重点是检测不安全的网络和攻击,但对于CPS环境需要增强的解决方案.

研究的目的:

  • 引入一个新的框架,深袋式卷积神经网络与启发式多群群优化 (DCNN-HMACO),用于提高网络物理系统的安全性.
  • 增强CPS网络内的安全信息传输,效率和便利性.
  • 有效地检测和减轻网络物理系统中的攻击.

主要方法:

  • 开发深层包装卷积神经网络与启发式多群殖民地优化 (DCNN-HMACO) 框架.
  • 实施先进的深度学习和优化技术,用于攻击检测.
  • 与现有方法比较的比较分析,如卷积神经网络 (CNN) 和模糊C-Means (FCM).

主要成果:

  • 拟议的DCNN-HMACO框架显示了攻击检测率的显著改善.
  • 该框架增强了网络物理系统的整体系统保护.
  • 取得了92.14%的惊人的准确率,超过了现有的方法 (CNN为72.12%,FCM为79.56%).

结论:

  • DCNN-HMACO框架为提高网络物理系统安全性提供了卓越的解决方案.
  • 该框架有效检测攻击,确保更安全的数据传输和系统完整性.
  • 这种方法代表了保护CPS免受网络威胁的重大进步.