在IIoT环境中,SHAP增强的变压器GWO提升模型用于透明和强大的异常检测
Mohammed Aly1, Naif M Alotaibi2
1Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, Badr City, 11829, Egypt. mohammed-alysalem@eru.edu.eg.
Scientific reports
|November 20, 2025
概括
本研究介绍了一种混合人工智能框架,用于检测工业物联网 (IIoT) 系统中的异常. 该模型结合了时间变压器和灰狼优化器,用于在智能工厂中准确,实时的网络物理风险检测.
科学领域:
- 事物的工业互联网 (IIoT)
- 网络物理系统 网络物理系统
- 机器学习 机器学习
背景情况:
- 工业物联网 (IIoT) 的采用增加了工厂的实时监控和自动化.
- 工业物联网环境经常面临异常和网络物理风险.
- 传统的机器学习模型与不平衡的数据,时间依赖以及传感器流中的概念漂移作斗争.
研究的目的:
- 为IIoT传感器数据中准确检测异常提出混合框架.
- 通过将时间模式识别与强大的分类集成来增强异常检测.
- 为了提高模型的概括性和可解释性,用于工业应用.
主要方法:
- 一个混合框架,结合了时间变压器编码器和物流提升分类器.
- 生物灵感功能优化使用灰狼优化器进行功能选择.
- 对可解释异常检测输出进行SHAP分析.
主要成果:
- 在真实数据集上实现了98.2%的准确性,96.7%的精度,97.1%的回忆,F1得分为0.969和AUC为0.996.
- 在预测性能和对数据漂移的弹性方面表现优于基线的物流提振模型.
- 证明了适合边缘部署的低推理延迟,并提供可解释的异常警报.
结论:
- 混合框架有效地检测物联网启用工厂中的不寻常事件.
- 结合时间变压器,增强合奏和元启发式优化,可提供卓越的异常检测.
- 该框架可以适应不同的工厂,并可扩展到更大的数据集,以实现安全,适应性的工业系统.
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