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VAE-WACGAN:基于VAEGAN的改进数据增强方法,用于入侵检测.

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  • 1School of Information Engineering, Institute of Disaster Prevention, Beijing 101601, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
概括

本研究介绍了VAE-WACGAN,这是一个新的生成模型,用于打击网络入侵检测中的阶级不平衡. 它生成现实的少数类样本,增强检测模型性能和网络安全.

关键词:
数据集平衡数据集平衡深度学习是一种深度学习.生成性的对抗性网络.网络入侵检测系统 (IDS) 是一个网络入侵检测系统.网络安全 网络安全变量自动编码器变量自动编码器

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 网络入侵检测数据集中的类失衡会降低模型性能.
  • 现有的方法难以生成现实的少数群体类样本,以有效地平衡数据集.

研究的目的:

  • 提出一个新的生成模型,VAE-WACGAN,以解决网络入侵检测中的阶级不平衡.
  • 提高产生的少数阶级样本的质量,提高培训稳定性.

主要方法:

  • 通过整合变量自编码器生成对抗网络 (VAEGAN),辅助分类器生成对抗网络 (ACGAN) 和瓦斯斯坦生成对抗网络与梯度惩罚 (WGAN-GP) 开发了VAE-WACGAN.
  • 使用VAE-WACGAN进行过量采样异常数据以生成现实的合成异常.
  • 验证了UNSW-NB15和CIC-IDS2017数据集的方法.

主要成果:

  • VAE-WACGAN模型产生了高质量的合成异常,非常接近实际的网络流量分布.
  • 使用VAE-WACGAN进行过量采样有效地平衡了网络流量数据集.
  • 在平衡数据集上训练的入侵检测模型显示显著增强的性能指标.

结论:

  • VAE-WACGAN有效地解决了网络入侵检测数据集中的类不平衡问题.
  • 拟议的方法可以提高入侵检测模型的性能.
  • 基于VAE-WACGAN的入侵检测在网络安全方面比其他先进方法更有效.