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基于火算法的WSN-IoT安全增强,使用机器学习进行入侵检测.

M Karthikeyan1, D Manimegalai2, Karthikeyan RajaGopal3

  • 1Centre for Advanced Wireless Integrated Technology, Chennai Institute of Technology, Chennai, India. karthickm37@gmail.com.

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|January 3, 2024
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概括
此摘要是机器生成的。

本研究介绍了一种新的火算法机器学习 (FA-ML) 技术,用于在无线传感器网络 (WSN) 和物联网 (IoT) 中增强入侵检测. 该FA-ML方法达到99.34%的准确性,显著提高了WSN-IoT安全性.

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科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习应用 机器学习应用

背景情况:

  • 无线传感器网络 (WSN) 和物联网 (IoT) 越来越多地集成在一起,以进行增强的数据分析和自动化.
  • 保护这些相互连接的WSN和物联网系统对于可靠性和安全性至关重要.
  • 现有的安全措施需要改进,以应对集成WSN-IoT环境的复杂性.

研究的目的:

  • 为WSN-IoT系统开发和评估一种新的安全技术.
  • 通过机器学习和生物灵感算法的协同作用来提高入侵检测的准确性.
  • 为相互连接的网络引入新的以安全为导向的优化方法.

主要方法:

  • 提出了一种火算法机器学习 (FA-ML) 技术用于入侵检测.
  • 使用支持矢量机 (SVM) 模型进行分类.
  • 使用灰狼优化器 (GWO) 算法对SVM模型进行参数调整.
  • 使用NSL-KDD数据集进行模拟的实验评估.

主要成果:

  • 该FA-ML技术实现了最大入侵检测准确率为99.34%.
  • 这显著优于其他模型,KNN-PSO达到96.42%的精度,XGBoost达到95.36%的精度.
  • 证明了将机器学习与火算法相结合的有效性,以获得强大的安全性.

结论:

  • 该FA-ML技术代表了WSN-IoT安全方面的重大进步.
  • 它提供了一种强大而智能化的解决方案,用于增强入侵检测能力.
  • 这些发现验证了生物灵感算法和机器学习在保护现代互联系统方面的潜力.