# Deep Learning-Based Efficient Analysis for Encrypted Traffic
## 3 System Framework of Encrypted Traffic Identification
### Data Padding and Truncation for Encrypted Traffic
The confusion matrix generated by the C-LSTM model, as depicted in Figure 10, allows us to observe the classification results. In this matrix, each row represents the traffic type, while each column corresponds to the predicted traffic type of the C-LSTM model. It is worth noting that Chat-type traffic is misclassified as Email-type due to similarities in traffic characteristics or the presence of similar application types within the actual traffic. This poses challenges in accurately identifying carried traffic solely based on analysis. To further enhance model accuracy, we can consider incorporating additional information beyond individual packet analysis for multimodality-based traffic identification. During the training of both the CNN model and the C-LSTM model, we closely monitored the trend of the cross-entropy loss function values with each batch iteration. As depicted in Figure 11, both models exhibited a gradual reduction in their loss function values, indicating continuous learning and performance improvement. However, it is worth noting that the C-LSTM model displayed a notably faster rate of convergence in the reduction in the loss function. In comparison with the CNN model, it appeared to converge to a lower loss value more swiftly. This observation suggests that the C-LSTM model may possess certain advantages in leveraging sequential information and long-term dependencies, resulting in quicker learning and adaptability. Figure 9: Confusion matrix for the CNN model. The proposed CNN model in this study achieved a commendable level of precision and recall, highlighting its exceptional performance in accurately classifying and detecting intricate patterns within the dataset. Moreover, it outperforms traditional machine learning algorithms such as the C4.5 algorithm and the KNN algorithm, which rely on manually selected data features. Furthermore, the performance of the C-LSTM model was found to be superior to that of a single CNN model, as depicted in Figure 12. It shows better performance on the file transfer type and VPN-encrypted traffic type, which validates the effectiveness of the LSTM method in extracting packet temporal features. Figure 11: The computational efficiency of models. Figure 10: Confusion matrix for the C-LSTM model. We present the hyperparameters used in the two deep learning models in Figure 5:0 After training and testing on an open-source dataset, the accuracy rate achieved was 96.4%. To verify the effectiveness of the C-LSTM model developed in this study for extracting spatial and temporal features of data packets, we conducted a quantitative comparison with other methods. ## 5 Conclusions

In this study, we delved deeply into the identification of encrypted traffic on the Internet. We observed that while encrypted communication enhances user privacy protection, it also presents challenges in traffic identification. Although mature traffic identification techniques have performed well both domestically and internationally, including deep packet inspection and traditional machine learning methods, they exhibit certain limitations in effectively identifying encrypted traffic. To address this issue, we propose an encrypted traffic identification method based on C-LSTM that can automatically preprocess traffic data and effectively address the inherent issues in traditional methods. Initially, we constructed recognition models based on CNN and achieved remarkable results. Subsequently, we refined the model and developed a model based on C-LSTM. Our experiments demonstrate that our proposed system significantly enhances the accuracy and efficiency of encrypted traffic identification by effectively extracting spatial and temporal features from encrypted traffic packets. In future research, we plan to further partition original traffic streams or sessions to achieve higher granularity with the potential integration of a real-time traffic collection assessment system's generalization capability. This research received no external funding. Not applicable.