电子学报2024，Vol.52Issue(3)：783-791,9.DOI:10.12263/DZXB.20220924
基于改进随机森林的工业互联网安全态势评估方法
Method of Security Situation Assessment Based on Improved Random Forest for Industrial Internet
摘要
Abstract
Aiming at the difficulties of data feature extraction and low accuracy of industrial Internet security situa-tion assessment method,a method of security situation assessment based on improved random forest for industrial Internet is proposed.The original data set is balanced based on random sampling technique to reduce the influence of unbalanced da-ta set on the experiment.The gradient boosting decision tree is used to determine the weight coefficients of different fea-tures in industrial Internet traffic data,and the key features are extracted by the recursive feature elimination method.Con-struct a multi-classification attack detection model for the industrial Internet based on improved random forest,identify the types of attacks on the network,and determine the degree of risk in combination with the quantitative indicators of security situation.The experimental results show that the detection accuracy and F1 score of this algorithm reach 89.19%and 89.68%respectively.Compared with the traditional random forest algorithm,support vector machine and k-nearest neigh-bor algorithm,the accuracy and F1 score are improved by at least 2.91%and 1.7%respectively,with an average increase of 8.38%and 9.33%.关键词
工业互联网/态势评估/特征提取/梯度提升决策树/随机森林Key words
industrial internet/situation assessment/feature extraction/gradient boosting decision tree/random forest分类
信息技术与安全科学引用本文复制引用
胡向东,万润楠..基于改进随机森林的工业互联网安全态势评估方法[J].电子学报,2024,52(3):783-791,9.基金项目
重庆市高校创新研究群体(No.CXQT20016) Chongqing University Innovation Research Group(No.CXQT20016) （No.CXQT20016）
