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基于多模型融合堆叠集体学习的电源监控系统的恶意流量检测方法
Hao Zhang1, Ye Liang2, Yuanzhuo Li1
1State Grid Jibei Electric Power Co., Ltd., Beijing 100054, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了多模型融合方法,用于检测电源监控系统中的恶意网络流量. 与单个机器学习模型相比,这种堆叠策略显著提高了检测精度和稳定性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络安全 网络安全
背景情况:
- 像电源监控系统这样的关键基础设施面临着因互联网恶意流量的增加而不断升级的网络威胁.
- 传统的基于规则的检测方法不足以应对复杂多样化的现代网络攻击.
- 现有的用于交通检测的单个机器学习模型存在普遍性差,准确性低,不稳定.
研究的目的:
- 为电源监控系统提出一种先进的恶意流量检测方法.
- 克服网络安全中单一机器学习模型的局限性.
- 为了提高恶意流量检测的概括性,稳定性和准确性.
主要方法:
- 使用堆叠技术实施了多模型融合策略.
- 集成多个机器学习模型,创建一个强大的检测系统.
- 在NSL-KDD数据集上评估了拟议的方法.
主要成果:
- 堆叠模型在NSL-KDD测试套件上实现了96.5%的准确性.
- F1的得分达到96.6%,表明高精度和回忆.
- 记录了1.8%的低虚假阳性率,证明了可靠性.
结论:
- 使用堆叠的多模型融合显著改善了电力系统中的恶意流量检测.
- 拟议的方法比单一模型方法提供了更高的概括性和稳定性.
- 这项研究验证了组合方法在加强关键基础设施网络安全方面的有效性.
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