Network Security Situation Factor Extraction Based on Random Forest of Information Gain
Yongcheng Duan, Xin Li, Xue Song Yang, Le Yang · 2019
Aiming at the problem of situational element extraction, a method based on random forest of information gain for network security situation factor extraction is proposed. First, the importance of the attribute is determined by the information gain. After the threshold is set, the attribute is reduced and the redundant attribute is deleted. Secondly, the processed data is classified using the random forest classifier. Finally, in order to verify the efficiency of the algorithm, the improved method is tested by the intrusion detection data set. Compared with the traditional method, the experimental results show that the algorithm effectively improves the accuracy and achieves efficient extraction of network security situation elements.