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使用深度分解机器进行SCADA入侵检测

Mohammed Zakariah¹, Syed Umar Amin², Fatma S Alrayes³

¹Department of Computer Science and Engineering, College of Applied Studies, King Saud University, P.O. Box 22459, 11495, Riyadh, Saudi Arabia.

Scientific reports

|November 13, 2025

概括

本研究介绍了一种深度因子化机器 (DeepFM),用于在工业物联网 (IIoT) 环境中的监督控制和数据采集 (SCADA) 系统中检测网络攻击. 与传统技术相比,DeepFM方法显著提高了入侵检测的准确性和性能.

科学领域:

网络安全网络安全
工业控制系统工业控制系统
机器学习机器学习

背景情况:

集成到工业物联网 (IIoT) 的监督控制和数据采集 (SCADA) 系统容易受到复杂的网络攻击.
传统的入侵检测方法与IIoT网络威胁的高维度和复杂模式作斗争.
基于签名,基于统计异常和经典机器学习等现有技术不足以应对现代的IIoT安全挑战.

研究的目的:

提出一种基于深度因子化机器 (DeepFM) 的新型入侵检测方案,适用于IIoT环境中的SCADA系统.
利用DeepFM的能力来建模低级特征交互和高级表示,以改进攻击检测.
评估拟议的DeepFM方案在各种SCADA数据集中的有效性和通用性.

主要方法:

开发一个深度分解机 (DeepFM) 框架,将分解机和深度神经网络结合起来.
集成DeepFM用于在SCADA网络流量中建模复杂特征交互.
在四个基准数据集上对DeepFM方案的测试和验证:WUSTL-IIoT-2018,WUSTL-IIoT-2021,HAI Security和Sherlock.

主要成果:

在WUSTL-IIoT-2018数据集中,DeepFM实现了近乎完美的准确性 (99.98%) 和F1得分 (0.9997).
在WUSTL-IIoT-2021 (98.72%准确率,0.9945 F1得分) 和HAI (95.6%准确率,0.967精度,0.973回忆) 观察到高性能.
该模型在Sherlock数据集上表现出强的性能,准确率为95.4%,F1得分为0.955,优于传统方法.

结论:

基于DeepFM的入侵检测方案对于IIoT环境中的SCADA系统非常有效和准确.
DeepFM表现出灵活性,弹性和可扩展性,使其适用于广泛的工业系统.
拟议的方法为提高IIoT安全性的传统方法提供了一种实用且优越的替代方案.

关键词:

深度因子化机器深度因子化侵入者攻击是一种入侵攻击.侵入检测系统的入侵检测系统机器学习机器学习这是一个 SCADA 系统.

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