基于XGBoost算法的智能电网信息攻击识别模型

TM744; 智能电网在遭受信息攻击后,如何根据量测数据的变化规律,准确识别电力系统遭受的攻击类型是提高电网安全防御的有效手段,提出一种基于Extreme Gradient Boosting(XGBoost)算法的智能电网信息攻击识别模型.基于kmeans-smote设计电力数据过采样方法,对量测数据进行平衡处理,解决攻击事件样本的不平衡问题.提出最大相关-最小冗余(MRMR)特征选择方法,提取信息攻击事件最优表征特征子集,降低数据维度并提升信息攻击的识别效率.设计XGBoost分类器,对3种攻击状态和正常状态进行分类识别,采用准确率、召回率等指标评估模型的识别性能.经仿真实验验证,所提出的信...

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Published in电测与仪表 Vol. 60; no. 1; pp. 64 - 86
Main Authors 邬蓉蓉, 黎新, 宾冬梅
Format Journal Article
LanguageChinese
Published 广西电网有限责任公司电力科学研究院,南宁530023 15.01.2023
Subjects
特征选择
信息攻击识别
XGBoost算法
过采样
智能电网
Online AccessGet full text
ISSN1001-1390
DOI10.19753/j.issn1001-1390.2023.01.010

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Abstract TM744; 智能电网在遭受信息攻击后,如何根据量测数据的变化规律,准确识别电力系统遭受的攻击类型是提高电网安全防御的有效手段,提出一种基于Extreme Gradient Boosting(XGBoost)算法的智能电网信息攻击识别模型.基于kmeans-smote设计电力数据过采样方法,对量测数据进行平衡处理,解决攻击事件样本的不平衡问题.提出最大相关-最小冗余(MRMR)特征选择方法,提取信息攻击事件最优表征特征子集,降低数据维度并提升信息攻击的识别效率.设计XGBoost分类器,对3种攻击状态和正常状态进行分类识别,采用准确率、召回率等指标评估模型的识别性能.经仿真实验验证,所提出的信息攻击识别模型显著提升了智能电网信息攻击的识别精度,且具有较好的泛化性.
AbstractList TM744; 智能电网在遭受信息攻击后,如何根据量测数据的变化规律,准确识别电力系统遭受的攻击类型是提高电网安全防御的有效手段,提出一种基于Extreme Gradient Boosting(XGBoost)算法的智能电网信息攻击识别模型.基于kmeans-smote设计电力数据过采样方法,对量测数据进行平衡处理,解决攻击事件样本的不平衡问题.提出最大相关-最小冗余(MRMR)特征选择方法,提取信息攻击事件最优表征特征子集,降低数据维度并提升信息攻击的识别效率.设计XGBoost分类器,对3种攻击状态和正常状态进行分类识别,采用准确率、召回率等指标评估模型的识别性能.经仿真实验验证,所提出的信息攻击识别模型显著提升了智能电网信息攻击的识别精度,且具有较好的泛化性.
Author 黎新
宾冬梅
邬蓉蓉
AuthorAffiliation 广西电网有限责任公司电力科学研究院,南宁530023
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Bin Dongmei
Wu Rongrong
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Keywords 特征选择
信息攻击识别
XGBoost算法
过采样
智能电网
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Snippet TM744; 智能电网在遭受信息攻击后,如何根据量测数据的变化规律,准确识别电力系统遭受的攻击类型是提高电网安全防御的有效手段,提出一种基于Extreme Gradient...
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Title 基于XGBoost算法的智能电网信息攻击识别模型
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