Research on new energy station network security assessment method based on improved LSTM network

LIU Shan; LI Rui; WANG Yao

doi:10.11959/j.issn.1000-0801.2024226

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Research on new energy station network security assessment method based on improved LSTM network

Research and Development | 更新时间：2024-12-02

- Research on new energy station network security assessment method based on improved LSTM network
- Telecommunications Science Vol. 40, Issue 10, Pages: 124-133(2024)
- 作者机构：
  
  1.国网山西省电力公司电力科学研究院，山西太原 030001
  2.国网山西省电力公司，山西太原 030021
- 作者简介：
- 基金信息：
  
  State Grid Shanxi Electric Power Company Science and Technology Project(520530230009)
- DOI：10.11959/j.issn.1000-0801.2024226
  CLC： TM73
- Received：07 June 2024，
  
  Revised：13 October 2024，
  
  Published：20 October 2024
- 稿件说明：
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刘珊,李瑞,王尧.基于改进长短期记忆网络的新能源场站网络安全评估方法研究[J].电信科学,2024,40(10):124-133.

LIU Shan,LI Rui,WANG Yao.Research on new energy station network security assessment method based on improved LSTM network[J].Telecommunications Science,2024,40(10):124-133.
刘珊,李瑞,王尧.基于改进长短期记忆网络的新能源场站网络安全评估方法研究[J].电信科学,2024,40(10):124-133. DOI： 10.11959/j.issn.1000-0801.2024226.

LIU Shan,LI Rui,WANG Yao.Research on new energy station network security assessment method based on improved LSTM network[J].Telecommunications Science,2024,40(10):124-133. DOI： 10.11959/j.issn.1000-0801.2024226.

摘要

为了解决新能源大规模并网造成现有新能源场站网络安全防护体系无法满足网络异常监测和告警需求的问题，提出一种基于改进长短期记忆网络的新能源场站网络安全评估方法。首先，根据新能源场站网络系统架构，分析网络安全发生原因；其次，基于随机森林算法求解新能源场站网络流量的基尼系数，进而求出网络流量所有特征的重要系数，选出重要特征；最后，将重要特征输入长短期记忆网络中，利用注意力机制自适应分配数据的时间和特征，加强对网络流量中重要时间和特征的重视，进而提高模型对网络安全评估的准确性。试验结果表明，该方法能够准确评估新能源场站网络安全状态，与支持向量机、卷积神经网络、传统长短期记忆网络相比，评估准确率分别提升了12.65%、9.34%、8.79%，提升了新能源电力系统的网络安全状态感知、评价和告警能力。

Abstract

In order to solve the problem of the inability of the existing network security protection system for new energy stations to meet the needs of network anomaly monitoring and alarm caused by the large-scale integration of new energy

a new energy station network security assessment method based on an improved long short-term memory network was proposed. Firstly

based on the architecture of the new energy station network system

the reasons for network security incidents were analyzed. Secondly

based on the random forest algorithm

the Gini coefficient of new energy station network traffic was solved

and then the important coefficients of all network traffic features were calculated to select important features. Finally

important features were input into the long short-term memory network

and attention mechanisms were used to adaptively allocate data time and features

strengthening the emphasis on important time and features in network traffic

thereby improving the accuracy of the model for network security assessment. The experimental results show that this method can accurately evaluate the network security status of new energy power stations. Compared with support vector machines

convolutional neural networks

and traditional long short-term memory networks

the evaluation accuracy has been improved by 12.65%

9.34% and 8.79%

respectively

enhancing the perception

evaluation

and alarm capabilities of network security status in new energy power systems.

关键词

Keywords

references

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