ET-Mamba: A Mamba Model for Encrypted Traffic Classification

With the widespread use of encryption protocols on network data, fast and effective encryption traffic classification can improve the efficiency of traffic analysis. A resampling method combining Wasserstein GAN and random selection is proposed for solving the dataset imbalance problem, and it uses Wasserstein GAN for oversampling and random selection for undersampling to achieve class equalization. Based on Mamba, an ultra-low parametric quantity model, we propose an encrypted traffic classification model, ET-Mamba, which has a pre-training phase and a fine-tuning phase. During the pre-training phase, positional embedding is used to characterize the blocks of the traffic grayscale image, and random masking is used to strengthen the learning of the intrinsic correlation among the blocks of the traffic grayscale image. During the fine-tuning phase, the agent attention mechanism is adopted in the feature extraction phase to achieve global information modeling at a low computational cost, and the SmoothLoss function is designed to solve the problem of the insufficient generalization ability of cross-entropy loss function during training. The experimental results show that the proposed model significantly reduces the number of parameters and outperforms other models in terms of classification accuracy on non-VPN datasets.