This article chooses to use the Random Forest algorithm to improve the performance of network intrusion detection systems. The algorithm significantly improves the accuracy, recall and precision of network intrusion detection compared to traditional methods. The required data and experimental results were obtained from the LUFlow dataset by using a more accurate feature extraction method. Eventually, the readability and comprehension of the experimental results were enhanced by visualizing them. Overall, the performance of the network intrusion detection system based on the random forest method has been significantly improved. However, there are still some problems in the experiment, such as the lack of comparison with other commonly used intrusion detection methods or algorithms. Similar problems make the experiment lack of comprehensiveness. Therefore, future research should consider introducing more kinds of intrusion detection methods for comparative analysis to further validate and improve the performance of the system. In addition, extending the dataset of the experiments and improving the feature extraction techniques may also bring additional improvements. In summary, although the performance of the random forest-based network intrusion detection system has been improved, there is still much room for improvement and research potential.

Intrusion Detection System,Random Forest,Feature Selection,Categorization