Analysis of an enhanced random forest algorithm for identifying encrypted network traffic

Abstract

The focus of this paper is to apply an improved machine learning algorithm to realize the efficient and reliable identification and classification of network communication encrypted traffic, and to solve the challenges faced by traditional algorithms in analyzing encrypted traffic after adding encryption protocols. In this study, an enhanced random forest (ERF) algorithm is introduced to optimize the accuracy and efficiency of the identification and classification of encrypted network traffic. Compared with traditional methods, it aims to improve the identification ability of encrypted traffic and fill the knowledge gap in this field. Using the publicly available datasets and preprocessing the original PCAP format packets, the optimal combination of the relevant parameters of the tree was determined by grid search cross-validation, and the experimental results were evaluated in terms of performance using accuracy, precision, recall and F1 score, which showed that the average precision was more than 98 %, and that compared with the traditional algorithm, the error rate of the traffic test set was reduced, and the data of each performance evaluation index were better, which It shows that the advantages of the improved algorithm are obvious. In the experiment, the enhanced random forest and traditional random forest models were trained and tested on a series of data sets and the corresponding test errors were listed as the basis for judging the model quality. The experimental results show that the enhanced algorithm has good competitiveness. These findings have implications for cybersecurity professionals, researchers, and organizations, providing a practical solution to enhance threat detection and data privacy in the face of evolving encryption technologies. This study provides valuable insights for practitioners and decision-makers in the cybersecurity field