Research on network intrusion detection method for industrial control system based on CNN-LSTM-Attention

LI Di; YANG Dong; WANG Wenqing; DENG Nanyi; LIU Pengfei; CUI Yiqun; LIU Chaofei; ZHU Bodi

doi:10.19666/j.rlfd.202401014

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Research on network intrusion detection method for industrial control system based on CNN-LSTM-Attention

更新时间：2025-09-28

- Research on network intrusion detection method for industrial control system based on CNN-LSTM-Attention
- Thermal Power Generation Vol. 53, Issue 5, Pages: 115-121(2024)
- 作者机构：
  
  西安热工研究院有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202401014
  CLC： TM73;TP273;TP393.08
- Published：2024
- 稿件说明：
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LI Di, YANG Dong, WANG Wenqing, et al. Research on network intrusion detection method for industrial control system based on CNN-LSTM-Attention[J]. Thermal power generation, 2024, 53(5): 115-121.
DOI：

LI Di, YANG Dong, WANG Wenqing, et al. Research on network intrusion detection method for industrial control system based on CNN-LSTM-Attention[J]. Thermal power generation, 2024, 53(5): 115-121. DOI： 10.19666/j.rlfd.202401014.

摘要

随着各类网络攻击事件的增加，能源电力基础设施中工业控制系统安全问题也逐渐成为人们关注的焦点。结合电力系统的特点，提出一种融合卷积神经网络（convolutional neural network

CNN）、长短时记忆（long short-term memory

LSTM）神经网络和注意力（Attention）机制的CNN-LSTM-Attention网络入侵检测算法模型，通过在实验室仿真环境中构造和采集600 MW燃煤机组制粉系统在3种典型工况下受到网络攻击的运行状态数据集，对所提出的检测算法模型进行训练和评估。结果表明：相较于CNN、LSTM模型，所提出的入侵检测算法模型性能最优；模型准确率、精确率、召回率等评级指标均为最好，综合评价优于其他的入侵检测方法。该入侵检测算法模型具有较强的创新性和实用性。

Abstract

With the increase of various types of cyber-attacks

the security of industrial control systems in energy and power infrastructures has gradually become a focus of attention. Combined with the characteristics of power system

the CNN-LSTM-Attention network intrusion detection algorithm model integrating convolutional neural network(CNN)

long and short-term memory(LSTM) neural network and Attention mechanism is proposed. By constructing and collecting the operating state data sets of the pulverizing system of a 600 MW coal-fired unit under three typical operating conditions under cyber-attacks in a laboratory simulation environment

the proposed detection algorithm model is trained and evaluated. The results show that

the proposed intrusion detection algorithm model has the best performance compared with the CNN and LSTM models. The model has the best rating indexes such as accuracy

precision

recall

etc.

and the comprehensive evaluation is better than other intrusion detection methods. The intrusion detection algorithm model is highly innovative and practical.

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山西华光发电有限责任公司

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School of Control and Computer Engineering, North China Electric Power University

China Power Investment Corporation Zhuhai Hengqin Thermal Power Co., Ltd

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