Method of Security Situation Assessment Based on Improved Random Forest for Industrial Internet
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%.
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