## 2 Related Works  
Packet loss is essentially a case of data loss, and the requirements for data integrity are different in various scenarios. It is necessary to set appropriate reconstruction targets according to different application scenarios. In the current research, two kinds of reconstruction methods are employed:  
**Reconstruction for precise data,** which means filling the original value of the missing bytes, such as the data imputation algorithms [29; 30; 31; 32]. The classic data imputation methods, including middle value imputation, mean value imputation, k-nearest neighbors, and decision trees, are often used for comparison [33; 34]. In recent years, the autoencoder (AE) and its variants have often been applied to the data imputation field due to their properties concerning their ability of learning from corrupted data [35]. The commonly used evaluation indexes of data imputation methods, including Mean Square Error (MSE), mainly focus on the difference between the original data and reconstructed data. However, in data imputation studies, most of the study object comprises tabular data, which are quite different from the network traffic data. Tabular data are multidimensional and structured. In most cases, each dimension of the tabular data represents a feature, and each feature has a clear meaning. However, network traffic is one-dimensional and cannot be segmented into multiple features with clear semantics. When imputing tabular data, the type and meaning of the missing data is clear, and the length of a single missing value is relatively short. However, network traffic data vary greatly, and it is difficult to determine the meaning of the missing parts. When packet loss happens, the length of the missing values equals the length of the lost packets, which are much larger than the missing tabular value. Thus, some of the classical methods that are suitable for tabular data, such as mean value imputation and middle value imputation, are not applicable for network traffic since these methods are too simple. **Reconstruction for characteristics,** which means reconstructing the characteristics of the missing data. The mainstream method of analysing raw traffic data is the deep learning method, which is mainly based on the potential characteristics of network traffic [36]. Figure 2: How TCReC works in an edge computing network. Therefore, learning the deep characteristics of the network traffic and reconstructing the missing part at the character level rather than the original byte level can reduce the difficulty of data reconstruction. Some deep learning-based models can effectively extract and reconstruct the characteristics of the data and are mainly used in the field of natural language processing (NLP) and computer vision (CV). BERT (Bidirectional Encoder Representation from Transformer) [37] proposed by [PERSON] et al. in 2018 adopts a masked language model (MLM), which randomly covers or replaces words in sentences during training. BERT can generate deep bi-directional linguistic representation and predict the covered or replaced words. Masked Autoencoder (MAE) [38] proposed by [PERSON] et al. in 2021 learns image features by randomly masking image patches. It performs work similar to image restoration in the pre-training stage and reconstructs lost pixels in the output. The evaluation method of these kinds of methods is usually closely related to practical application, such as image recognition and language classification. However, in the field of network traffic analysis, there is no widely used method or model that tries to reconstruct the characteristics of the missing network flow data. This study proposes TCReC, which reconstructs the characteristics of the lost packets. To better describe the reconstruction effect in the application scenarios of TCReC, we design an evaluation index that illustrates TCReC's capability under practical network traffic classification experiments.