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Design and Analysis for Fall Detection System Simplification
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可解释的基于深度学习的特征选择和入侵检测方法在物联网上.

Xuejiao Chen1, Minyao Liu2, Zixuan Wang2

  • 1School of Communications, Nanjing Vocational College of Information Technology, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
概括

本研究介绍了网络入侵检测系统 (NIDS) 的可解释特征选择方法. 通过使用SHAP和因果关系,它提高了模型可靠性,并减少了复杂性,以提高物联网中的安全性.

关键词:
在RFE中,使用RFE是必要的.这就是 SHAP SHAP 的意思.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.功能选择 功能选择获取信息获取信息模型的解释性可解释性随机的森林随机的森林

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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:

  • 物联网 (IoT) 需要强大的网络安全,网络入侵检测系统 (NIDS) 是至关重要的.
  • 深度学习 (DL) 提高了NIDS的性能,但在解释性和计算效率方面面临挑战.
  • 功能选择 (FS) 对于通过减少参数和开销来优化NIDS中的DL模型至关重要.

研究的目的:

  • 提出一种可解释的特征选择方法,用于检测加密网络流量的入侵.
  • 为解决基于DL的NIDS的模型解释性和轻量化部署的挑战.
  • 通过有效的特征选择,提高NIDS的可靠性和效率.

主要方法:

  • 开发了一种可解释的特征选择方法,整合了SHAP (夏普利添加式扩展) 和因果关系原则.
  • 利用模型解释结果指导特征选择过程,减少特征维度.
  • 评估了使用卷积神经网络 (CNN) 和随机森林 (RF) 模型对CICIDS2017和NSL-KDD数据集的方法.

主要成果:

  • 拟议的可解释特征选择方法在入侵检测方面表现出卓越的性能.
  • 减少功能数量,同时保持或提高NIDS的可靠性和性能.
  • 在不同数据集和机器学习模型 (CNN,RF) 中验证的有效性.

结论:

  • 基于SHAP和因果关系的特征选择为基于DL的NIDS提供了可靠和可解释的解决方案.
  • 这种方法有效地平衡了模型复杂性和检测性能,用于实际的NIDS部署.
  • 该方法有助于在物联网时代推进安全和高效的网络入侵检测.