SeniorSentry:基于相关性和相互信息的情境异常检测,用于现场衰老
Achyuth Nandikotkur1, Issa Traore1, Mohammad Mamun2
1Deptartment of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8P 5C2, Canada.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
物联网 (IoT) 传感器之间的相关性可以检测攻击和故障. 使用这些相关性的一种新模型在智能家居中检测异常时实现了89.47%的真实阳性率.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 越来越多地使用物联网 (IoT) 传感器在智能家居中进行辅助生活,需要采取强有力的安全措施.
- 现有的安全协议可能无法充分解决传感器故障或针对物联网设备的复杂攻击.
研究的目的:
- 调查和确认物联网智能家居环境中同位传感器之间的相关性.
- 开发和验证一种新的上下文异常检测模型,以增强物联网智能家居安全.
主要方法:
- 对两个公共智能家居数据集和一个定制收集的数据集进行统计分析.
- 采用移动窗口方法与监督机器学习相结合,用于异常检测.
- 使用真正正比率 (TPR) 和假正比率 (FPR) 评估模型的性能.
主要成果:
- 在同一位置的物联网传感器之间确认了统计学上显著的线性和非线性相关性.
- 开发了一个异常检测模型,实现了89.47%的真实阳性率.
- 该模型显示了0%的错误阳性率,表明在检测异常时的高精度.
结论:
- 物联网智能家居中的传感器相关性是异常和攻击检测的可行属性.
- 拟议的上下文异常检测技术有效提高了物联网智能家居系统的安全性.
- 这项研究为更安全,更可靠的物联网支持的现场老化解决方案提供了基础.
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