# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 3 Methodology
### Transformer  
In the architectural design of TransECA-Net, the Transformer [25] module achieves advanced semantic modeling through multi-stage feature interaction. During the model design phase, we conducted systematic evaluations of Transformer variants based on the  
Figure 2: ECANet structural flowchart. characteristics of encrypted traffic classification tasks. Although Perceiver reduces computational complexity through cross-attention mechanisms, its global feature interaction capability is constrained by latent-space mapping processes, which may compromise the fine-grained pattern capture of byte-level traffic sequences. While MobileViT excels in mobile vision tasks, its inherent spatial inductive bias introduces architectural mismatches with the temporal nature of network traffic. By contrast, the multi-head self-attention mechanism of the standard Transformer establishes full-connectivity dependencies between byte features at arbitrary positions, which is crucial for identifying discriminative yet dispersed protocol fingerprints in encrypted traffic. The technical implementation involves the following workflow:  
First, the H \(\times\) W \(\times\) C 3D feature maps extracted from the CNN backbone network (where H and W denote spatial dimensions and C represents channel count) undergo spatial dimension flattening to transform into N \(\times\) (H \(\times\) W) sequence representations (N being batch size), adapting to Transformer's sequential processing paradigm. This sequence is then fed into a multi-head self-attention mechanism with four parallel attention heads. Each sub-head employs learnable linear projections to map input features into 64-dimensional query (Q), key (K), and value (V) vector spaces. Global dependency matrices across spatial positions are established through scaled dot-product attention computation. The outputs from all attention heads are concatenated along the channel dimension and reconstructed via a learnable linear transformation matrix (4 heads \(\times\) 64 dimensions) to form enhanced feature representations with global contextual information. The attention output first undergoes residual addition with input features, followed by layer normalization. The normalized features are processed through a feed-forward network (FFN) comprising two fully connected layers, with ReLU activation for non-linear transformation in the intermediate layer. Both core components (MSA and FFN) integrate dropout regularization applied before residual connections to enhance model generalization. This cascaded architecture design leverages multi-head attention to capture long-range spatial dependencies while coordinating with local FFN refinement. The residual learning strategy effectively mitigates gradient vanishing issues. Through these mechanisms, the Transformer module achieves global modeling and representation enhancement of input features, providing high-quality feature representations for subsequent classification tasks. Unlike previous models that relied on RNNs and LSTMs, the Transformer exclusively leverages the attention mechanism to handle temporal dependencies in data, without using any recurrent layers. In contrast to the CNN + LSTM network traffic classification method employed by [PERSON] [26] and [PERSON] [23], replacing the LSTM with a Transformer module offers significant advantages, particularly in handling large-scale and high-dimensional time-series data. Although LSTMs are designed to manage long-range dependencies, they often struggle to retain information from earlier time steps when processing very long sequences. By contrast, the self-attention mechanism of the Transformer allows it to effectively handle long-range dependencies, as each element in the sequence can directly interact with all other elements without needing to propagate information iteratively through multiple time steps. In terms of parallel computing capabilities, LSTMs are constrained by their recursive nature, which limits parallelization.