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Research on network intrusion detection method of power system based on random forest algorithm

机译：基于随机林算法的电力系统网络入侵检测方法研究

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摘要

Aiming at the problem of low detection accuracy in traditional power system network intrusion detection methods, in order to improve the performance of power system network intrusion detection, a power system network intrusion detection method based on random forest algorithm is proposed. Firstly, the power system network intrusion sub sample is selected to construct the random forest decision tree. The random forest model is optimized by using the edge function. The accuracy of the vector is judged by the minimum state vector of the power system network, and the measurement residual of the power system network attack is calculated. Finally, the power system network intrusion data set is clustered by Gaussian mixture clustering Through the design of power system network intrusion detection process, the power system network intrusion detection is realized. The experimental results show that the power system network intrusion detection method based on random forest algorithm has high network intrusion detection performance.
机译：针对传统电力系统网络入侵检测方法检测精度低的问题，为了提高电力系统网络入侵检测的性能，提出了一种基于随机林算法的电力系统网络入侵检测方法。首先，选择电力系统网络入侵子样本来构建随机林决策树。随机林模型通过使用边缘功能进行了优化。通过电力系统网络的最小状态向量判断向量的准确性，并计算电力系统网络攻击的测量残余。最后，通过电力系统网络入侵检测过程的设计通过高斯混合聚类聚集了电力系统网络入侵数据集，实现了电力系统网络入侵检测。实验结果表明，基于随机林算法的电力系统网络入侵检测方法具有高网络入侵检测性能。

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