# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
###### Abstract  
Encrypted network traffic classification remains a critical component in network security monitoring. However, existing approaches face two fundamental limitations: (1) conventional methods rely on manual feature engineering and are inadequate in handling high-dimensional features; and (2) they lack the capability to capture dynamic temporal patterns. This paper introduces TransECA-Net, a novel hybrid deep learning architecture that addresses these limitations through two key innovations. First, we integrate CCA-Net modules with CNN architecture to enable automated feature extraction and efficient dimension reduction via channel selection. Second, we incorporate a Transformer encoder to model global temporal dependencies through multi-head self-attention, supplemented by residual connections for optimal gradient flow. Extensive experiments on the ISCX VPN-nonVPN dataset demonstrate the superiority of our approach. TransECA-Net achieved an average accuracy of 98.25% in classifying 12 types of encrypted traffic, outperforming classical baseline models such as 1D-CNN, CNN + LSTM, and TFE-GNN by 6.2-14.8%. Additionally, it demonstrated a 37.44-48.84% improvement in convergence speed during the training process. Our proposed framework presents a new paradigm for encrypted traffic feature disentanglement and representation learning. This paradigm enables cybersecurity systems to achieve fine-grained service identification of encrypted traffic (e.g., 98.9% accuracy in VPN traffic detection) and real-time responsiveness (48.8% faster than conventional methods), providing technical support for combating emerging cybercrimes such as monitoring illegal transactions on darknet networks and contributing significantly to adaptive network security monitoring systems. caption =1  
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significantly enhancing network-architecture optimization efficiency. The emergence of hybrid encryption systems--integrating the efficacy of symmetric encryption with the security provisions of public-key encryption--coupled with hashing functions and digital signature technologies has established a multi-layered security defense mechanism for contemporary network communications. It is imperative to recognize that the widespread implementation of encryption technologies [9] has given rise to new security challenges, as malicious actors exploit privacy-enhancing tools for illicit transactions conducted on the dark web, including arms and drug trafficking, thereby complicating the task of traffic tracing considerably. Traditional classification methodologies encounter three principal limitations: port/protocol-based methods have become obsolete in the face of dynamic port technologies; Deep Packet Inspection (DPI) [10] grapples with issues of privacy violation and high computational costs; and statistical feature classification is heavily reliant on extensive volumes of labeled data. While machine-learning approaches [11; 12], such as Decision Trees and Random Forests, overcome the dependency on protocols, they remain constrained by the financial burden associated with the acquisition of labeled data. Although deep learning technologies [13] facilitate automatic feature extraction, they still encounter challenges relating to the consumption of substantial computational resources and limited accuracy in the classification of encrypted traffic. To address these challenges, this paper introduces the TransECA-Net model, which innovatively integrates CNN, Transformer, and ECANet [14] architectures. The model employs an ECANet-enhanced CNN module to facilitate automatic feature extraction and efficient dimensionality reduction, incorporates a Transformer to establish comprehensive global spatiotemporal relationships, and utilizes residual connections to promote optimal gradient flow. In the experimental section, we leverage the publicly available ISCX dataset to juxtapose TransECA-Net against four other models.