基于XGBoost算法的智能电网信息攻击识别模型

Network attack identification model of smart grid based on XGBoost algorithm

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。

After the smart grid suffers from information attacks,how to accurately identify the attack type of the power system according to the change law of the measured data is an effective means to improve the security defense of the power grid.Aiming at the above problems,this paper proposes a smart grid network attack identification model based on extreme gradient boosting(XGBoost)algorithm.The power data oversampling method is designed based on Kmeans-smote to balance the measured data and solve the imbalanced problem of attack event samples.Then,based on the feature selection method of maximum correlation and minimum redundancy(MRMR),the optimal feature subset of information attack events is extracted to reduce the data dimension and improve the recognition efficiency of information attack.Finally,XGBoost classifier is designed to classify and recognize three kinds of attack states and normal states,and the identification performance of the model is evaluated by accuracy and recall rate.The experimental results prove that the network attack identification model improves the detection accuracy of smart grid information attacks significantly,which has good generalization.