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论文信息:

  • 2020
  • IEEE
  • 异常检测+时间序列+CNN+Autoencoder(LSTM)+DNN

本篇论文是在上一篇《Web traffic anomaly detection using C-LSTM neural networks》的基础上进行的,本篇作者在两个方面进行了改进:
1.数据预处理方面;
2.模型方面

(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

一、概括

  • 研究对象
  • 目标
  • 方法
  • 实验
  • 结果

(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

二、相关的工作

  • IoT应用场景
  • 异常数据形成的原因
  • 常见异常检测方法
  • 回顾上篇论文的方法
  • 作者的改进(贡献)

(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

三、作者的研究方法

  • 数据预处理方面
  • 模型改进方面
  • 实验方面
  • 总结及展望
  • 两篇论文的比较

(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

四、论文笔记

(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
(九)Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

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