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在智能工业机械厂使用物联网和机器学习的异常检测
Angel Jaramillo-Alcazar1, Jaime Govea1, William Villegas-Ch1
1Escuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías Ciencias Aplicadas, Universidad de Las Américas, Quito 170125, Ecuador.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究通过使用机器学习进行异常检测,提高了制造业的物联网安全性. 该系统提高了检测率,减少了假阳性,提高了工厂的安全性和效率.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 工业物联网工业物联网工业物联网
背景情况:
- 物联网 (IoT) 安全性在现代技术中至关重要.
- 智能制造工厂通过传感器和摄像头生成大量数据.
- 实时异常检测对于运营效率和安全至关重要.
研究的目的:
- 在智能制造工厂中实施和评估基于机器学习的安全解决方案.
- 提高异常检测率,减少物联网系统中的错误阳性.
- 评估加强安全对工厂效率和安全的影响.
主要方法:
- 从遥测传感器,物联网摄像头和控制设备收集历史数据.
- 训练有素的机器学习模型用于实时异常检测.
- 与以前的工业物联网安全研究中的性能指标进行了比较.
主要成果:
- 在异常检测率上取得了13%的改善.
- 将错误阳性率降低了3%.
- 对不寻常事件做出更快,更有效的反应,减少了关键事件.
结论:
- 基于机器学习的安全解决方案对于工业环境中的物联网系统非常有效.
- 实施的系统显著提高了工厂的效率,安全和资源利用.
- 安全解决方案可适应各种工业和商业应用.
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