计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 241200157-7.doi: 10.11896/jsjkx.241200157

• 信息安全 • 上一篇    下一篇

基于多源网络数据的电力监控系统入侵检测方法

蒋亚坤, 林旭   

  1. 云南电网有限责任公司云南电力调度控制中心 昆明 650000
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 蒋亚坤(15804324722@163.com)
  • 基金资助:
    中国南方电网有限责任公司科技项目(0500002023030301XT00152)

Intrusion Detection Method for Power Monitoring System Based on Multi-source Network Data

JIANG Yakun, LIN Xu   

  1. Yunnan Power Grid Corporation Yunnan Power Dispatch Control Centre,Kunming 650000,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    China Southern Power Grid Co.,Ltd.(0500002023030301XT00152).

摘要: 随着电力系统信息化、网络化、智能化建设的不断推进,电力监控系统面临着日益严峻的网络安全威胁。综合考虑网络资产安全风险、用户行为、业务特征等多方面因素,对电力监控系统网络所涵盖的多源数据进行全面深入分析显得尤为重要。据此提出了电力监控系统多源数据清洗方法及入侵检测方法,利用改进最大相关-最小冗余算法对电力监控系统网络的多源安全数据特征进行选择,保留合适的安全数据特征,利用网络入侵检测模型实现多源网络安全数据的检测与分类,有效解决电力监控系统多源数据特征属性复杂导致后期模型分类准确度下降等问题。仿真实验证明,所提出的多源数据特征选择方法与入侵检测算法对电力监控系统攻击的检测率与分类准确率均有明显提高。

关键词: 电力监控系统, 长短期记忆神经网络, 特征处理, 入侵检测, 数据清洗

Abstract: With the continuous advancement of informatization,networking,and intelligence in the power system,the power monitoring system is facing increasingly severe network security threats.It is particularly important to conduct a comprehensive and in-depth analysis of the multi-source data covered by the power monitoring system network,taking into account various factors such as network asset security risks,user behavior,and business characteristics.Based on this,a multi-source data cleaning me-thod and intrusion detection method for power monitoring systems are proposed.The improved maximum correlation and minimum redundancy algorithm is used to select the multi-source security data features of the power monitoring system network,retain appropriate security data features,and use a network intrusion detection model to detect and classify multi-source network security data,effectively solving the problem of complex feature attributes of multi-source data in power monitoring systems leading to decreased accuracy of model classification in the later stage.Simulation experiments show that the proposed multi-source data feature selection method and intrusion detection algorithm have significantly improved the detection rate and classification accuracy of attacks on power monitoring systems.

Key words: Power monitoring system, Long short-term memroy, Feature processing, Intrusion detection, Data cleaning

中图分类号: 

  • TP309
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