基于机器学习的网络入侵检测方法研究  

Research on Network Intrusion Detection Method Based on Machine Learning

作　　者：孙玉坤 韩聿彪 SUN Yukun;HAN Yubiao(Sinochem Transportation Construction Group Operation Management(Shandong)Co.,Ltd.,Jinan 250014,China;Shandong Electronic Information Products Inspection Institute,Jinan 250014,China)

机构地区：[1]中化学交通建设集团运营管理(山东)有限公司,山东济南250014 [2]山东省信息技术产业发展研究院,山东济南250014

出　　处：《通信电源技术》2023年第2期184-186,190,共4页Telecom Power Technology

摘　　要：考虑到传统方法在检测网络入侵数据时存在准确率、检测率和F1分数低的问题,提出了基于机器学习的网络入侵检测方法。根据网络入侵数据传输量的变化情况,估计出网络入侵数据的传输量,通过初始化机器学习算法的参数,获得网络入侵数据提取结果的概率矩阵,将网络入侵数据检测的特征向量作为机器学习算法的输入,构建网络入侵检测模型,实现了网路入侵的检测。实验结果表明,所提方法在检测网络入侵数据的过程中可以有效提高检测的检测率和F1分数,具有更好的检测性能。Considering the low accuracy,detection rate and F1 score of traditional methods in detecting network intrusion data,a network intrusion detection method based on machine learning is proposed.According to the change of network intrusion data transmission,the transmission amount of network intrusion data is estimated.By initializing the parameters of machine learning algorithm,the probability matrix of network intrusion data extraction results is obtained.The feature vector of network intrusion data detection is used as the input of machine learning algorithm to build a network intrusion detection model,and network intrusion detection is realized.The experimental results show that the method in this paper can effectively improve the detection rate and F1 score in the process of detecting network intrusion data,and has better detection performance.

关 键 词：机器学习算法 网络入侵 特征提取 传输量 检测方法 观测向量 

分 类 号：TN929.11[电子电信—通信与信息系统] TP311.13[电子电信—信息与通信工程]