基于优化支持向量回归的工业互联网安全态势预测方法

胡向东; 吕高飞; 白银

doi:10.12263/DZXB.20210558

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基于优化支持向量回归的工业互联网安全态势预测方法

学术论文 | 更新时间：2025-12-08

- 基于优化支持向量回归的工业互联网安全态势预测方法
- A Method of Security Situation Prediction for Industrial Internet Based on Optimized Support Vector Regression
- 电子学报 2023年51卷第2期页码：446-454
- 作者机构：
  
  1.重庆邮电大学自动化学院，重庆 400065
  2.重庆邮电大学先进制造工程学院，重庆 400065
- 作者简介：
  
  [ "胡向东男，1971年出生，四川广安人.重庆邮电大学教授，博士生导师.主要研究方向为智能感知、网络化测量及工业互联网安全，物联网安全智能理论与技术，复杂系统建模、仿真与优化等.E-mail： huxd@cqupt.edu.cn" ]
  [ "吕高飞男，1995年出生，河南洛阳人.主要研究方向为工业互联网安全." ]
- 基金信息：
  
  教育部-中国移动科研基金(MCM20150202;MCM20180404);重庆市高校创新研究群体(CXQT20016)
- DOI：10.12263/DZXB.20210558
  中图分类号： TP391.9;TN918.91
- 收稿：2021-04-30，
  
  修回：2022-01-04，
  
  纸质出版：2023-02-25
- 稿件说明：
移动端阅览
胡向东,吕高飞,白银.基于优化支持向量回归的工业互联网安全态势预测方法[J].电子学报,2023,51(02):446-454.

HU Xiang-dong,LÜ Gao-fei,BAI Yin.A Method of Security Situation Prediction for Industrial Internet Based on Optimized Support Vector Regression[J].ACTA ELECTRONICA SINICA,2023,51(02):446-454.
胡向东,吕高飞,白银.基于优化支持向量回归的工业互联网安全态势预测方法[J].电子学报,2023,51(02):446-454. DOI： 10.12263/DZXB.20210558.

HU Xiang-dong,LÜ Gao-fei,BAI Yin.A Method of Security Situation Prediction for Industrial Internet Based on Optimized Support Vector Regression[J].ACTA ELECTRONICA SINICA,2023,51(02):446-454. DOI： 10.12263/DZXB.20210558.

摘要

作为支撑智能制造等的新型工业基础设施，工业互联网的安全态势预测是一个关键性需求和应用新挑战.本文提出一种基于优化支持向量回归的工业互联网安全态势预测方法，即利用差分进化算法和自适应参数调整策略克服灰狼优化算法计算速度慢、优化精度低的缺点；再利用改进的灰狼优化算法优化支持向量回归参数；最后，利用最优化参数组合建立支持向量回归预测模型，实现工业互联网环境下的安全态势预测.仿真实验结果表明，在容许偏差为0.05或0.1时，本文方法的预测准确率分别为90%和100%，预测结果的绝对误差均小于0.07，相比于对比方法有更高的预测准确率和预测精度.

Abstract

The Industrial Internet is an emerging modern infrastructure for supporting smart manufacturing. Accurate security situation prediction of industrial Internet is nowadays still a key demand and challenge for industrial application. To this aim

a novel method of security situation prediction for industrial Internet based on optimized support vector regression is proposed in this paper. The proposed method is a three-step procedure: in the first step

an improved gray wolf optimizer algorithm

based on differential evolution and adaptive parameter adjustment strategy

with high calculation speed and optimization accuracy is proposed; then

the optimized parameters of support vector regression are obtained; after that

accurate security situation prediction model for industrial Internet is established. The simulation results show that the prediction accuracy rate of the proposed method are 90% and 100% when the allowable deviations are 0.05 or 0.1

respectively

and the corresponding absolute errors are less than 0.07

and thus the proposed method can enhance the accuracy rate and precision of prediction

in contrast to the existing methods.

关键词

Keywords

references

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