網絡流量分類對於網絡安全至關重要，因為它有助於檢測異常流量和潛在的攻擊模式，從而實現及時的防護措施。隨著遠程工作、視頻會議和 VPN 服務的廣泛應用，網絡流量的快速增長增加了流量分類的複雜性。傳統方法，如基於端口的分類、深度封包檢查（DPI）和機器學習（ML），在處理複雜和加密流量時存在局限性。在本研究中，我們提出了一個新穎的網絡流量分類系統，該系統結合了 1D-CNN 和 Transformer 來提取局部和長距離特徵，並融合這些互補特徵以提高分類能力。使用 ISCX VPNnonVPN 數據集的實驗結果顯示，與基線方法相比，我們提出的系統在大多數類別中的準確率、召回率和 F1 分數上均具有更好的表現。;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 surpasses baseline methods across most categories with respect to Precision, Recall, and F1-score.
