基于多元线性回归模型的网络安全漏洞预测研究

Research on Network Security Vulnerability Prediction Based on Multiple Linear Regression Model

使用监督学习中的多元线性回归算法设计并实现了网络安全漏洞预测模型。在设计模型时,通过原始数据采集、样本特征选择构建了标准数据集;利用数据集的样本数据输入多元线性回归函数构建模型,使用最小二乘算法估算回归系数,通过在不同子数据集上的多次重复训练,提高模型的准确性和稳定性;最后使用Python实现了该模型的主要功能。比较多次实验结果发现,该模型预测的漏洞数据值与真实值相近,可通过参考漏洞数据,预先做好相应的保护措施,从而降低网络风险,保护用户隐私。

In this paper,the multi linear regression algorithm in supervised learning is used to design and implement the network security vulnerability prediction model.In designing,the standard data set is firstly constructed by the original data collection and sample feature selection;then,the model is constructed by inputting the sample data into the multiple linear regression function,and the regression coefficient is estimated by the least square algorithm.Through repeated training on different sub data sets,the accuracy and stability of the model are improved;finally,Python is used to realize the main functions of the model.Compared with the experimental results,it can be found that the predicted vulnerability data value of the model is similar to the real value.The corresponding protection measures can be done in advance by referring to the vulnerability data,so as to reduce the network risk and protect the user privacy.