基于SAE-LSTM的工艺数据异常检测方法

尚文利; 石贺; 赵剑明; 曾鹏

doi:10.12263/DZXB.20180015

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基于SAE-LSTM的工艺数据异常检测方法

学术论文 | 更新时间：2025-12-08

- 基于SAE-LSTM的工艺数据异常检测方法
- An Anomaly Detection Method of Process Data Based on SAE-LSTM
- 电子学报 2021年49卷第8期页码：1561-1568
- 作者机构：
  
  1.广州大学电子与通信工程学院，广东广州 510006
  2.中国科学院沈阳自动化研究所，辽宁沈阳 110016
  3.中国科学院大学，北京 100039
  4.中科院网络化控制系统重点实验室，辽宁沈阳 110016
- 作者简介：
  
  [ "尚文利男，1974年生于黑龙江省望奎县.博士，研究员，博士生导师.现为广州大学“百人计划”学科带头人，原中国科学院沈阳自动化研究所工业控制系统信息安全方向学术带头人.主要研究方向为工业控制系统信息安全、计算智能与机器学习、边缘计算.E-mail:shangwl@gzhu.edu.cn" ]
  [ "石贺男，1993年生于辽宁省沈阳市.中国科学院大学硕士研究生.研究方向为机器学习、数据挖掘、工业控制系统信息安全.E-mail:shihe@sia.cn" ]
- 基金信息：
  
  国家自然科学基金(61773368);之江实验室开放课题资助(2021KF0AB06)
- DOI：10.12263/DZXB.20180015
  中图分类号： TP393.08;TP39
- 收稿：2018-12-29，
  
  修回：2021-05-31，
  
  纸质出版：2021-08-25
- 稿件说明：
移动端阅览
尚文利,石贺,赵剑明等.基于SAE-LSTM的工艺数据异常检测方法[J].电子学报,2021,49(08):1561-1568.

SHANG Wen-li,SHI He,ZHAO Jian-ming,et al.An Anomaly Detection Method of Process Data Based on SAE-LSTM[J].ACTA ELECTRONICA SINICA,2021,49(08):1561-1568.
尚文利,石贺,赵剑明等.基于SAE-LSTM的工艺数据异常检测方法[J].电子学报,2021,49(08):1561-1568. DOI： 10.12263/DZXB.20180015.

SHANG Wen-li,SHI He,ZHAO Jian-ming,et al.An Anomaly Detection Method of Process Data Based on SAE-LSTM[J].ACTA ELECTRONICA SINICA,2021,49(08):1561-1568. DOI： 10.12263/DZXB.20180015.

摘要

为解决工业网络安全防护中工艺数据异常检测误报率较高的问题，本文提出一种基于时间序列的异常检测方法.该方法对工艺数据进行相关性分析、向量映射等处理，再采用堆叠自编码神经网络（SAE）对工艺数据特征进行降维，根据工艺数据在传输序列间的相互关联性，设计基于长短期记忆神经网络（LSTM）的异常检测模型，最后进行工艺数据异常检测仿真实验验证分析.实验结果表明，基于时间序列的异常检测模型能有效提高工艺数据异常检测准确率，并且误报率要低于传统隐马尔可夫异常检测模型，同时获得较好的异常检测实时性.

Abstract

In order to solve the problem of high false alarm rate of abnormal detection of process data in industrial network security protection

this paper proposes an anomaly detection method based on time series. In this method

the process data is analyzed by association analysis and vector mapping

and the stacked auto-encoder neural network (SAE) is used to reduce the dimension of process data features. According to the correlation of process data in the transmission sequence

an anomaly detection model based on long and short term memory neural network (LSTM) is designed. Finally

the simulation analysis of abnormal detection of process data is carried out. The experimental results show that the anomaly detection model based on time series can greatly improve the accuracy of process data anomaly detection

and the false positive rate is lower than the traditional hidden Markov anomaly detection model

and at the same time get better real-time performance of anomaly detection.

关键词

Keywords

references

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