INTRUSION DETECTION BASED ON IMPROVED RANDOM FOREST ALGORITHM IN SOFTWARE DEFINED NETWORK

In view of the large differences in the characteristics of intrusion data streams in softwaredefined networks and the applicability of the random forest algorithm in intrusion detection, this paper proposes an intrusion detection model based on an improved random forest algorithm. We analyzed the differences in the characteristics of intrusion data based on the Fisher ratio, and conducted feature partitioning according to their corresponding values. A weighted voting method was introduced to increase the weight of the decision tree with better classification performance. The node was split based on the maximum information gain rate. The grid search algorithm was improved to further improve the effect of random forest parameter optimization. Through experimental analysis, the accuracy, F1 value, AUC value and other evaluation indicators of this model is significantly improved, which verifies the effectiveness of the improved algorithm.