An Advanced Hybrid Model for Network Traffic Classification

Network traffic classification is crucial for cybersecurity, as it helps detect abnormal traffic and potential attack patterns, enabling timely protective measures. With the widespread adoption of remote work, video conferencing, and VPN services, the rapid growth of network traffic has increased the complexity of traffic classification. Traditional methods, such as port-based classification, Deep Packet Inspection, and Machine Learning (ML), face limitations in handling complex and encrypted traffic. In this study, we propose a novel network traffic classification system that combines 1D-CNN and Transformer, utilizing different activation functions to extract both local and long-range features. These complementary features are fused together to improve classification capability. The experimental results on the ISCX VPN-nonVPN dataset indicate that our proposed system outperforms baseline methods in most categories with respect to Precision, Recall, and F1-score.