## 5 Discussion
### Limitations and Future Work  
1. computational Overhead:  
**Issue:** Maintaining both a teacher and a student model increases computational complexity and memory usage. This can be a limitation in resource-constrained environments. This is because both models require operations such as parameter updates, forward propagation, and backpropagation, which will consume more computing resources. Moreover, storing the model parameters will also occupy more memory space. On some embedded devices or edge devices with limited computing power, such additional resource consumption may prevent the model from running properly or significantly reduce its operating efficiency, making it difficult to meet application requirements such as real-time performance. **Future Work:** To mitigate this issue, we plan to apply model compression techniques such as pruning, quantization, and knowledge distillation. Pruning reduces the number of model parameters by removing unimportant connections or neurons, thereby decreasing computational complexity and the memory footprint. Quantization converts the data representation of model parameters and computations from high precision to low precision, significantly reducing memory usage and computational load without significantly affecting model performance to a certain extent. Knowledge distillation transfers the knowledge learned by the teacher model to the student model, enabling the student model to achieve good performance even at a smaller scale. Through the application of these techniques, we can reduce the size and complexity of both models, enabling them to be more widely applied in resource-constrained environments, especially in real-time scenarios where efficient operation under limited resources can be achieved. 2. Data Requirements:  
**Issue:** The Mean Teacher framework relies on a large amount of unlabeled data to effectively leverage its semi-supervised learning capabilities. If the amount of unlabeled data is insufficient, the benefits of the framework may not be fully realized. The core of semi-supervised learning lies in using the information contained in a large amount of unlabeled data to assist the model in learning. When the amount of unlabeled data is inadequate, the model has difficulty capturing sufficiently rich data distribution characteristics, resulting in its inability to fully explore the underlying patterns in the data, which in turn affects the model's generalization ability and performance improvement. In practical application scenarios, it is sometimes difficult to obtain a large amount of high-quality unlabeled data, which limits the advantages of the Mean Teacher framework. **Future Work:** We aim to use data augmentation techniques to generate additional labeled and unlabeled data. Data augmentation can expand the diversity of data by performing operations such as flipping, rotating, and adding noise to existing data, allowing the model to learn more abundant data features. Additionally, we will explore synthetic data generation methods to create realistic and diverse traffic patterns. This can not only increase the amount of data but also enrich the variety of data, helping the model better adapt to different real-world scenarios, improving the model's generalization ability and mitigating the adverse impact of limited labeled data. 3. Generalization Across Different Traffic Types:  
**Issue:** Although our model combines CNN, BiLSTM, and Mean Teacher architectures, it efficiently extracts various features of encrypted traffic and reduces the dependence on labeled data through semi-supervised learning. However, the generalization ability of the model to new types of encrypted traffic unseen during training still faces some challenges. Training Data Diversity: If new types of encrypted traffic differ significantly from the types seen in training data, the model may struggle to classify them effectively. This is because the training process of the existing model depends on labeled data, and it cannot guarantee perfect performance on unseen traffic.