基于自编码器和隔离森林的水处理系统递进式异常检测方法

胡向东; 刘浪

doi:10.12263/DZXB.20230704

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基于自编码器和隔离森林的水处理系统递进式异常检测方法

学术论文 | 更新时间：2025-12-11

- 基于自编码器和隔离森林的水处理系统递进式异常检测方法
- A Progressive Abnormal Detection Method for Water Treatment System Based on Autoencoder and Isolation Forest
- 电子学报 2024年52卷第11期页码：3823-3834
- 作者机构：
  
  1.重庆邮电大学现代邮政学院，重庆 400065
  2.重庆邮电大学自动化学院/工业互联网学院，重庆 400065
- 作者简介：
  
  [ "胡向东男，1971年生，四川广安人.博士，重庆邮电大学教授，博士生导师.主要研究方向为智能感知、网络化测量与工业互联网安全等. E-mail: huxd@cqupt.edu.cn" ]
  [ "刘浪男，1999年生，重庆江津人.重庆邮电大学硕士研究生.主要研究方向为工业互联网安全. E-mail: liul1652725@163.com" ]
- 基金信息：
  
  重庆市高校创新研究群体(CXQT20016)
- DOI：10.12263/DZXB.20230704
  中图分类号： TN918.91;TP183
- 收稿：2023-07-26，
  
  修回：2024-02-02，
  
  纸质出版：2024-11-25
- 稿件说明：
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胡向东, 刘浪. 基于自编码器和隔离森林的水处理系统递进式异常检测方法[J]. 电子学报, 2024, 52(11): 3823-3834.

HU Xiang-dong, LIU Lang. A Progressive Abnormal Detection Method for Water Treatment System Based on Autoencoder and Isolation Forest[J]. Acta Electronica Sinica, 2024, 52(11): 3823-3834.
胡向东, 刘浪. 基于自编码器和隔离森林的水处理系统递进式异常检测方法[J]. 电子学报, 2024, 52(11): 3823-3834. DOI：10.12263/DZXB.20230704

HU Xiang-dong, LIU Lang. A Progressive Abnormal Detection Method for Water Treatment System Based on Autoencoder and Isolation Forest[J]. Acta Electronica Sinica, 2024, 52(11): 3823-3834. DOI：10.12263/DZXB.20230704

摘要

集成了工业互联网技术的水处理系统随着信息化程度的加深而面临着愈加严峻的异常行为入侵挑战.针对传统异常检测方法常用单一阈值检测、检测准确率低、误报率高等问题，提出一种融合自编码器和隔离森林的水处理系统递进式异常检测方法.首先，通过降采样过滤重复数据，加快递进式异常检测模型的训练和测试效率；其次，构建自编码器隐含层神经元捕捉数据关键

特征，优化自编码器的权重和偏置，设定重构误差阈值作为输入与重构之间的差异度量进行基础性检测；最后，构建以平均路径长度为异常度量阈值的隔离树并生成隔离森林，针对基础性检测发现的异常数据进一步遍历隔离树完成高级检测；基于两阶段递进式异常检测提升检测效果.实验结果表明，本文方法在安全水处理系统数据集下的异常检测准确率、

值均超过95%，准确率相比于传统方法平均提升31.86个百分点，特别是异常检测误报率被较大幅度降至0.30%.对配水系统数据集进行泛化性分析取得的精确率、召回率等指标均超过94%.模型的训练和测试时间相较于对比方法具有综合性能上的突出优势.

Abstract

With the deepening of informatization of water treatment systems integrated industrial internet technology are facing increasingly severe challenges of abnormal behavior intrusion. Aiming at such problems as single threshold detection

low detection accuracy

high false alarm rate and so on in traditional anomaly detection methods

a progressive anomaly detection method for water treatment systems that integrates autoencoders and isolation forests is proposed. Firstly

by downsampling to filter duplicate data

the training and testing efficiency of the progressive anomaly detection model is accelerated; Secondly

the hidden layer neurons of the autoencoder are constructed to capture the key features of the data

optimize the weight and bias of the autoencoder

and set the reconstruction error threshold as the difference measurement between input and reconstruction for basic detection; Finally

construct an isolation tree with the average path length as the anomaly measurement threshold to form an isolation forest

and further traverse the isolation tree to complete advanced detection based on the anomaly data discovered by basic detection; Improving detection performance based on two-stage progressive anomaly detection. The experimental results show that the accuracy and

score of the proposed method in the secure water treatment dataset exceeds 95%

compared with the traditional method

the accuracy is improved by 31.86 percentage points on average

especially

the false positive rate of anomaly detection is significantly reduced to 0.30%. The precision rate

recall rate and other indicators obtained by the

generalization analysis of the water distribution dataset are all over 94%. The training and testing time of the model has outstanding advantages in terms of comprehensive performance compared to comparative methods.

关键词

Keywords

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