增强的灰狼优化 (EGWO) 和基于随机森林的机制,用于在物联网网络中检测入侵
Saad Said Alqahtany1, Asadullah Shaikh2,3, Ali Alqazzaz4
1Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, 42351, Saudi Arabia.
Scientific reports
|January 14, 2025
概括
本研究介绍了用于入侵检测系统 (IDS) 功能选择的增强灰狼优化 (EGWO). 该方法有效地识别了关键特征,增强了物联网 (IoT) 的网络安全.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习用于安全.
背景情况:
- 通过物联网 (IoT) 智能设备的扩散带来了重大的网络安全挑战.
- 传统的入侵检测系统 (IDS) 与物联网网络的动态和复杂的威胁格局作斗争.
- 有效的网络安全需要可靠和计算效率高的IDS解决方案.
研究的目的:
- 确定开发用于物联网环境的可靠和计算效率高的入侵检测系统 (IDS) 的关键特性.
- 实施一个增强的灰狼优化 (EGWO) 算法,以实现有效的特征选择 (FS).
- 提高网络通信中检测网络攻击的准确性和效率.
主要方法:
- 实现了特征选择 (FS) 的增强灰狼优化 (EGWO),以减少数据集的维度.
- 利用NF-ToN-IoT数据集来评估拟议的FS技术.
- 采用随机森林 (RF) 算法来使用选择的最佳特征对网络流量进行分类.
主要成果:
- 基于EGWO的FS方法成功地从43个特征中确定了23个特征的最佳子集.
- 随机森林分类器使用优化的功能实现了99.93%的高精度.
- 与最近的攻击检测和融合技术相比,提出的方法显示出更高的性能.
结论:
- 为FS开发的EGWO是一种高度有效的方法,可以提高物联网网络中的IDS性能.
- 结合EGWO和RF,为准确和高效的网络攻击检测提供了强大的解决方案.
- 优化的功能集显著提高了入侵检测系统的可靠性和计算效率.
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