Ensemble Voting for Enhanced Robustness in DarkNet Traffic Detection

The increasing prevalence of DarkNet traffic poses significant challenges for network security. Despite improvements in machine learning techniques, most of the existing studies have not applied appropriate ensemble voting models on newer datasets like CIC-Darknet 2020. Some noteworthy works include methodologies that use CNN with K-Means for the classification of zero-day applications with very high accuracy, or approaches using GAN for data augmentation and improvement of accuracy and training efficiency. Techniques, in most cases, however, are associated with low model interpretability and high computational complexity. This paper discusses the study of a Voting Classifier that combines both Random Forest and Gradient Boosting for the purpose of improving predictive accuracy in a classification task. The research will be conducted on a broad dataset with several features, where feature selection is applied to get the best input for the models chosen. The results of the experiment indicate that the Voting Classifier has far higher performance compared to any single classifier, with an accuracy of 99.90%, precision of 99.99%, recall of 99.45%, and an F1 score of 99.72%. This clearly indicates the strength of ensemble methods in handling a diverse set of patterns and raising the ability to classify, which is an important lesson for the further development of research in machine learning and models.