Deep Learning-Based Mixture-of-Experts Model for Enhanced Network Traffic Classification

Abstract

Traffic classification is crucial for various aspects of network management, including network security, Quality of Service (QoS), and resource allocation. While deep learning (DL) models have demonstrated effectiveness in traffic classification, each type of DL model captures different feature sets, potentially missing essential information. For example, a dense network might excel at identifying general patterns, while a CNN focuses on spatial features, and an LSTM captures temporal dependencies. However, relying on a single DL model may result in incomplete feature extraction. To address this limitation and enhance classification performance, we propose an ensemble model that combines three DL architectures: a dense network, a CNN, and an LSTM. This ensemble method leverages the strengths of each model, enabling comprehensive feature extraction that incorporates spatial, temporal, and generalized patterns. By integrating these diverse feature sets, our ensemble model improves traffic classification accuracy and enhances overall system performance.