优化整体机器学习模型用于工业物联网中的网络攻击分类
Batool Alabdullah1, Suresh Sankaranarayanan1
1Department of Computer Science, King Faisal University, Al-Ahsa, Saudi Arabia.
Frontiers in artificial intelligence
|January 28, 2026
概括
本研究介绍了优化的堆叠组合模型,用于检测工业控制系统 (ICS) 和物联网 (IoT) 环境中的网络威胁,实现高精度和效率.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 工业控制系统 (ICS) 是指工业控制系统.
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 工业控制系统 (ICS) 和物联网设备面临越来越多的网络威胁,特别是在石油和天然气等关键行业.
- 现有的用于检测网络攻击的机器学习 (ML) 方法往往缺乏计算效率,并依赖于二进制分类.
研究的目的:
- 建议和评估优化的堆叠组合模型,以在ICS和物联网环境中增强网络攻击检测.
- 为了减少计算开销,同时提高复杂的网络威胁的检测准确度.
主要方法:
- 开发了两个优化的堆叠组合模型,集成了各种基础学习者 (逻辑回归,额外树分类器,XGBoost,LGBM,RFC).
- 选择的模型来解决安全数据集中的挑战,如类不平衡,噪音和复杂的攻击模式.
- 对其利用不同决策边界和学习机制以改进检测的能力进行评估.
主要成果:
- 堆叠的Ensemble_2模型实现了97%的准确性,计算时间为54分钟.
- 在2017年CICIDS数据集中,堆叠的Ensemble_2表现优于堆叠的Ensemble_1并达到100%的准确性,AUROC为99%.
结论:
- 拟议的Stacked Ensemble_2模型提供了一个可扩展的实时解决方案,用于保护ICS和物联网环境.
- 与传统方法相比,在检测关键基础设施中的网络威胁的准确性和效率方面取得了显著的进步.
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