NETWORK INTRUSION DETECTION BASED ON RANDOM FOREST ALGORITHM

With the rapid development of Internet technology, network security problems are becoming increasingly serious, and the frequency and complexity of network attacks are increasing, posing a serious threat to personal privacy, corporate interests and even national security. For the problem of redundant feature interference and dimensional disaster in high-dimensional network traffic data, this paper compares the effectiveness of feature screening and dimensionality reduction techniques, such as ANOVA, chi-square test and PCA, respectively, for the removal of irrelevant features in high-dimensional network traffic data, and the experimental results show that PCA solves the problem of high complexity of high-dimensional data processing and effectively improves the classification performance and operational efficiency of the model. Therefore, this study innovatively proposes a hybrid intrusion detection model that integrates Principal Component Analysis (PCA) and Random Forest (RF), and adopts a grid search algorithm to automate the optimization of the hyper-parameter set of the Random Forest, and finally the model has an accuracy of 99.81% in the test set, which indicates that it performs well in classifying the attack and normal traffic. Overall, the model provides an efficient and accurate solution for network intrusion detection, which has important reference value for future research and practical applications.