在物联网中使用集合树用于尸网络检测和分类的比较分析
Mohamed Saied1, Shawkat Guirguis2, Magda Madbouly2
1Institute of Graduate Studies and Research, Alexandria, Egypt. igsr.msaied@alexu.edu.eg.
Scientific reports
|December 7, 2023
概括
基于树的机器学习算法显示了检测物联网 (IoT) 尸网络攻击的巨大潜力. 随机森林 (RF) 在实证研究中实现了卓越的检测准确性和计算性能.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网设备由于资源有限和多样性有限,容易受到尸网络攻击.
- 现有的机器学习 (ML) 和深度学习 (DL) 方法在实现物联网安全的高精度与合理的计算成本方面面临挑战.
- 尸网络攻击对物联网生态系统构成重大安全挑战.
研究的目的:
- 分析研究基于树的机器学习算法的性能,用于检测物联网中的尸网络攻击.
- 为了比较各种基于树的ML算法的检测能力和计算性能.
主要方法:
- 使用特定于物联网环境的公共尸网络数据集进行实证研究.
- 基本决策树算法的比较与集体学习方法 (包装和提升).
- 基于检测精度和计算性能对算法的评估.
主要成果:
- 基于树的ML算法显示出在物联网中检测网络入侵的巨大潜力.
- 随机森林 (RF) 算法实现了多类分类的最高性能,准确率为0.999991.1.
- 在所有其他评估的绩效指标中,RF也获得了最高的结果.
结论:
- 基于树的ML算法,特别是RF,对于物联网尸网络检测非常有效.
- 射频提供了一个有前途的解决方案,以提高物联网安全性,高精度和高效的计算.
- 对基于树的ML的进一步研究可以显著提高对物联网技术的信任和增长.
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