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适应型多目标进化型生成型对抗性网络用于Metaverse网络入侵检测.

Dikai Xu1,2, Bin Cao1,2

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此摘要是机器生成的。

适应式多目标进化生成对抗网络 (AME-GAN) 增强了Metaverse和物联网 (IoT) 设备的网络安全. 这种新的方法提高了入侵检测准确度和实时性能,以应对不断变化的网络威胁.

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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
  • 网络入侵检测 网络入侵检测

背景情况:

  • 超级宇宙和物联网 (IoT) 的融合创造了重要的网络安全漏洞,包括数据泄露和设备改.
  • 现有的入侵检测系统难以在动态的Metaverse环境中应对新出现的网络威胁.
  • 用于入侵检测的神经网络的手动设计是耗时的,并且通常会导致低于最佳性能.

研究的目的:

  • 提出一种新的,可扩展的解决方案,即自适应的多目标进化生成对抗网络 (AME-GAN),用于优化Metaverse中的网络入侵检测.
  • 为了解决Metaverse设备的网络安全方面的关键差距,传统方法经常忽视这些差距.
  • 为了提高不同硬件约束的入侵检测系统的准确性,实时性能和模型多样性.

主要方法:

  • 开发一个以注意力为基础的反比例混合长短期记忆GAN,以生成少数类样本并解决数据不平衡.
  • 实现适应性进化神经架构搜索算法,以指导GAN超网突变并提高训练稳定性.
  • 整合双重突变多目标进化神经架构搜索算法以优化准确性,实时性能和模型多样性.

主要成果:

  • 与最先进的方法相比,AME-GAN在NSL-KDD,UNSW-NB15和CIC-IDS2017数据集上表现出卓越的表现.
  • 在精度方面取得了0.32%,在F1得分方面取得了0.31%,在精度方面取得了0.47%,在回忆方面取得了0.37%.
  • 拟议的框架为Metaverse网络安全提供了增强的检测性能和实时适用性.

结论:

  • AME-GAN提供了一个有前途的,适应性框架,用于加强Metaverse中的网络安全.
  • 这项研究有助于为下一代数字环境推进网络入侵检测.
  • 开发的方法有效地解决了数据不平衡,并优化了对Metaverse应用程序至关重要的各种性能指标.