Is Deep Learning a Better Option than Random Forest for Encrypted Traffic Classification?

Our study challenges the conventional understanding that deep learning models consistently outperform traditional machine learning approaches in classification tasks. By evaluating the performance of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Random Forest (RF) models on QUIC traffic classification, we demonstrate that RF achieves superior accuracy, precision, recall, F1-score, and computational efficiency compared to CNN and LSTM. This finding underscores the importance of considering both performance and computational efficiency when selecting an appropriate model. Additionally, we emphasize the practical applicability of RF, especially in resource-constrained environments, where its efficiency makes it a compelling alternative to deep learning methods. These insights offer valuable guidance for enhancing network security, optimizing resource utilization, and deploying effective traffic classification systems in real-world scenarios.