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相关实验视频

Updated: Jun 23, 2025

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在无线传感器网络中提高入侵检测,使用GSWO-CatBoost方法.

Thuan Minh Nguyen1, Hanh Hong-Phuc Vo1, Myungsik Yoo2

  • 1Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括

一种新的遗传牺牲鱼优化 (GSWO) 方法提高了无线传感器网络 (WSN) 中的入侵检测系统 (IDS). GSWO-CatBoost实现了卓越的准确性,并且在实时威胁检测方面比深度学习快得多.

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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:

  • 侵入检测系统 (IDS) 对于无线传感器网络 (WSN) 的安全至关重要.
  • 有效的特征选择 (FS) 对IDS性能至关重要,但往往面临局限性.
  • 传统的方法可能会在过早的趋同和平衡勘探/开采方面扎.

研究的目的:

  • 为WSN入侵检测引入一种新的特征选择和分类方法.
  • 通过先进的优化和机器学习来提高IDS的有效性和效率.
  • 克服现有方法的局限性,特别是优化算法的过早融合.

主要方法:

  • 开发了遗传牺牲鱼优化 (GSWO) 算法,结合了遗传算法 (GA) 和鱼优化算法 (WOA).
  • 纳入了三种人口划分策略和条件遗传选择 (CIC),以改善WOA的全球搜索并避免过早的融合.
  • 使用CatBoost模型进行分类,并使用GSWO进行增强,通过量子化进行超参数微调.

主要成果:

  • 在多个基准数据集 (WSN-DS,WSNBFSF,NSL-KDD,CICIDS2017) 中,GSWO-CatBoost方法表现出卓越的性能.
  • 实现了高准确率:99.65% (WSN-DS),99.99% (WSNBFSF),99.76% (NSL-KDD) 和99.74% (CICIDS2017年) 的高准确率.
关键词:
功能选择 功能选择超参数优化超参数优化检测入侵 检测入侵机器学习是机器学习.无线传感器网络是无线传感器网络.

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  • 演示显著更快的推断时间,比深度学习方法快近100倍,表明实时适用性.

    • 结论:

    • 拟议的GSWO-CatBoost方法为WSNs的入侵检测提供了一个高度准确和高效的解决方案.
    • 新的GSWO算法有效地平衡了勘探和开发,增强了全球搜索能力.
    • 该方法在各种数据集中被证明是多功能和准确的,突出显示了它对网络安全的实际价值.