Application of Random Forest Model in Predictive Analysis of Network Security Big Data

Traditional protection measures are difficult to cope with in complex and changeable network attacks. This paper presents an innovative construction of a hybrid model based on the combination of graph neural network (GNN) and random forest (RF), and uses an adaptive feature selection mechanism and multi‐task learning strategy to carry out network security big data prediction analysis. Through experiments on CICIDS 2017 and KDD Cup 1999 datasets, the results show that the model has excellent performance in detecting various types of attacks, with an accuracy of up to 0.94 in DoS attack detection of the CICIDS 2017 dataset, and maintains strong robustness in different noise environments. The model performs outstandingly in training efficiency, generalization ability, and multi‐task learning performance, and significantly improves the accuracy and efficiency of network security threat prediction. Research shows that this model has opened up a new path for network security protection, has important value in dealing with complex network attack scenarios, and provides strong support for the development of the network security field.