A Network Security Situation Prediction Method for Intelligent Water Conservancy Based on MSCPO-BiGRU

XIA Zhuoqun; ZHOU Zihao; DENG Bin; KANG Chen

doi:10.11907/rjdk.251033

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A Network Security Situation Prediction Method for Intelligent Water Conservancy Based on MSCPO-BiGRU

Artificial Intelligence | 更新时间：2026-04-30

- A Network Security Situation Prediction Method for Intelligent Water Conservancy Based on MSCPO-BiGRU
- Software Guide Vol. 25, Issue 4, Pages: 89-97(2026)
- 作者机构：
  
  1.长沙理工大学计算机与通信工程学院
  2.长沙理工大学水利与环境工程学院
  3.湖南省水旱灾害防御事务中心网信技术部，湖南长沙 410000
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.251033
  CLC： TP183
- Received：11 March 2025，
  
  Published：15 April 2026
- 稿件说明：
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夏卓群,周子豪,邓斌等.一种基于MSCPO-BiGRU的智慧水利网络安全态势预测方法[J].软件导刊,2026,25(04):89-97.

XIA Zhuoqun,ZHOU Zihao,DENG Bin,et al.A Network Security Situation Prediction Method for Intelligent Water Conservancy Based on MSCPO-BiGRU[J].Software Guide,2026,25(04):89-97.
夏卓群,周子豪,邓斌等.一种基于MSCPO-BiGRU的智慧水利网络安全态势预测方法[J].软件导刊,2026,25(04):89-97. DOI： 10.11907/rjdk.251033.

XIA Zhuoqun,ZHOU Zihao,DENG Bin,et al.A Network Security Situation Prediction Method for Intelligent Water Conservancy Based on MSCPO-BiGRU[J].Software Guide,2026,25(04):89-97. DOI： 10.11907/rjdk.251033.

摘要

智慧水利是国家关键信息基础设施的核心行业与关键领域，对其网络安全态势预测技术进行研究可有效提升行业整体的安全防护能力与风险预警水平。为解决智慧水利网络安全态势预测的复杂性和多变性问题，提出一种基于双向门控循环单元（BiGRU）与多种群混沌粒子优化算法（MSCPO）的预测方法。首先，采用MSCPO算法寻优BiGRU的超参数，包括迭代次数、学习率和隐含层节点数；其次，使用优化后的BiGRU单元进行态势预测。在SWaT数据集上的实验结果表明，所提方法的均方误差为0.002 8，平均绝对误差为10.97%，决定系数为0.916 1。与传统算法相比，所提方法的预测准确性和稳定性更高，可为智慧水利网络安全建设提供有效支撑。

Abstract

Intelligent water conservancy serves as the core industry and key field of national key information infrastructure. Research on its network security situation prediction technology can effectively enhance the industry's overall security protection capability and risk early-warning level. To address the complexity and variability in predicting the security situation of intelligent water conservancy networks， a prediction method based on Bidirectional Gated Recurrent Unit （BiGRU） and Multi-Swarm Chaotic Particle Optimization Algorithm （MSCPO） is proposed. This method first employs the MSCPO algorithm to optimize the hyperparameters of BiGRU， including the number of iterations， learning rate， and number of hidden layer nodes. Subsequently， the optimized BiGRU unit is used for situation prediction. Experimental results on the SWaT dataset demonstrate that the proposed method achieves a mean square error of 0.002 8， an average absolute error of 10.97%， and a determination coefficient of 0.916 1. Compared with traditional algorithms， the proposed method exhibits higher prediction accuracy and stability， providing effective support for the construction of intelligent water conservancy network security.

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references

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