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Anomaly detection using depth learning on time series data related to application information

机译:异常检测使用与应用信息相关的时间序列数据的深度学习

摘要

A method and system for detecting and correcting anomalies in comparison with a previous time series segment of a sensor (204), a new time series segment generated by a sensor in a cyberphysical system Including generating similarity measures for each previous time series segment.Based on the similarity measure, it is determined that a new time series represents an abnormal behavior (206).Corrective measures are performed for the cyber physics system to correct the abnormal behavior (208).
机译:与传感器(204)的先前时间序列段相比检测和校正异常的方法和系统,该传感器中由网络物理系统中的传感器产生的新时序段,包括为每个先前的时间序列段生成相似度测量。基于 相似度测量,确定新的时间序列表示异常行为(206)。对网络物理系统执行的校正措施,以校正异常行为(208)。

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