## 1 Introduction
its neighboring elements. This observation suggests that novel approaches inspired by these fields could potentially be leveraged for network traffic reconstruction tasks. To test the validity of our model, we chose network traffic analysis, which has received academic attention and urgency in the edge computing field in recent years, as a downstream task [17; 18; 19]. Due to the low computing power of edge nodes, their security protection capabilities are also low, which makes them easy targets for attackers. According to Kaspersky researchers, the number of attacks against IoT devices rose to 15 billion in 2020-2021, an increase of more than 100% [20], and Gartner's annual report estimates that about 90% of IoT devices will be unmanaged and unprotected in 2025 [21]. This poses significant challenges to current network security. To address the growing number of network attacks, traffic analysis serves as an effective means to detect and mitigate these threats [22; 23]. Through traffic analysis, their ability to provide detection and surveillance of attacks throughout their life cycle enables a response to advanced persistent threats that can evade existing security measures. In this field, machine learning and deep learning methods have been widely adopted in the field of network traffic analysis in recent years [24; 25; 26]. These models use the pre-processed network data flow to train and classify the traffic to be analyzed to determine whether it is harmful traffic. However, such methods rely heavily on complete data. If significant packet loss occurs, models struggle to accurately classify traffic types due to insufficient information. Our experiments on the CIC-IDS-2017 dataset show that when packet loss reaches 70%, the F1-score of downstream deep learning models for traffic classification drops sharply, with a maximum decline of 73.86% (see Section 4.4 for detailed information). Therefore, it is very difficult to analyze network traffic when network traffic is lost, and it is more difficult for the model to find abnormal behavior in the network. To address these challenges, we have introduced TCReC, an innovative model specifically designed to reconstruct lost network traffic characteristics. Figure 2 illustrates the typical deployment scenario of deep learning methods in edge computing environments. In such scenarios, network traffic analysis tasks are deployed on edge servers or cloud servers, utilizing network traffic data for analysis and inference. When utilizing the proposed approach outlined in this paper, the reconstructed network traffic characteristics exhibit a remarkable similarity to the complete ones in terms of the deep learning model's comprehension. This approach enables the effective extraction of underlying characteristics from network data flows and intelligent identification of patterns, highlighting the efficacy of the proposed method in achieving accurate identification and understanding of network traffic characteristics. In summary, the main contributions of this study are as follows:  
1. We propose TCReC, a novel model for reconstructing network traffic characteristics through masked autoencoder-driven feature recovery, which effectively mitigates the adverse effects of packet loss on downstream analysis tasks in edge computing environments;
2. We employed t-SNE for dimensionality reduction on both original and reconstructed network traffic data, visualizing the 2D representations with scatter plots to depict traffic relationships, distances, and aggregations;
3. We introduce RAI, an evaluation metric, to quantify TCReC's performance in practical scenarios, which is independent of specific deep learning-based services;
4. We conduct extensive experiments on the ISCX-VPN-2016 [27], CIC-IDS-2017 [28] dataset, with the results showing that TCReC outperforms five other reconstruction methods, including Transformer, achieving the highest F1-score and RAI. The remainder of this paper is organized as follows: Section 2 introduces the related works of this study.