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功能重要性引导自动编码器用于减少入侵检测系统的维度.

Mohamed A Abdel-Rahman1, Ala Saleh Alluhaidan2, Sahar A El-Rahman3

  • 1Department of Computer Engineering and Systems, Ain Shams University, Cairo, Egypt. mohamed96elsheikh@gmail.com.

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概括

一个新的基于重要性的自动编码器 (FI-AE) 通过减少网络数据尺寸来增强入侵检测系统 (IDS). 与现有技术相比,这种方法提高了准确性和F1得分.

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自动编码器自动编码器缩小尺寸的缩小方式功能的重要性 功能重要性侵入检测系统的入侵检测系统网络安全 网络安全随机的森林随机的森林

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 网络安全 网络安全

背景情况:

  • 侵入检测系统 (IDS) 对于网络保护至关重要.
  • 缩小尺寸可以提高基于机器学习的IDS有效性.
  • 现有的方法可能无法完全优化IDS的功能选择.

研究的目的:

  • 为IDS.提出一个有效的维度减小技术.
  • 提高基于机器学习的IDS的准确性和性能.
  • 为网络入侵数据引入一种新的特征重要性方法.

主要方法:

  • 开发了一个基于特征重要性的自动编码器 (FI-AE).
  • 引入了一种使用随机森林的新型一对所有 (OVA) 特征重要性方法.
  • 训练了一个基于OVA特征重要性的权重损失函数的自动编码器.
  • 应用了自动编码器来减少维度,然后使用随机森林分类器.

主要成果:

  • FI-AE模型有效地减少了基准数据集中的特征维度.
  • 随机森林分类器实现了更高的准确性和F1得分,FI-AE减少了数据.
  • 在NSL-KDD,UNSW-NB15和CIC-IDS2017数据集上表现优于以前的缩小维度的技术.

结论:

  • 拟议的FI-AE技术是IDS的一个有效的尺寸缩小方法.
  • 将OVA功能重要性与自动编码器集成,可以提高IDS性能.
  • 这种方法为通过先进的机器学习提高网络安全提供了一个有希望的方向.