## 5 Conclusions and Future Work  
This paper investigates the significant impact of packet loss on the performance of deep learning-based network traffic analysis tasks, particularly in edge computing scenarios where wireless channel interference and limited bandwidth exacerbate data transmission challenges. Inspired by research on data imputation methods, we propose TCReC as an innovative approach to the reconstruction of the characteristics of lost packets in edge computing scenarios. Experimental evaluations on the CIC-IDS-2017 dataset demonstrate TCReC's robustness: even with 70% packet loss, downstream classifiers (CNN/LSTM)  
Figure 11: RAI of different models under different packet loss rates. trained on TCReC-reconstructed data retain F1-scores within 10% of the original performance, outperforming traditional methods like mean imputation (35-60% decline) and baseline models such as Transformer (12-25% decline). On the ISCX-VPN-2016 dataset, TCReC achieves a Reconstruction Ability Index (RAI) of 94.02% with LSTM classifiers. Comparative analyses using t-SNE visualization further confirm that TCReC preserves the distribution and clustering of traffic types with high fidelity. Additionally, we establish an evaluation index called Reconstruction Ability Index (RAI) to describe the reconstruction ability by connecting the reconstruction model with downstream tasks. Through extensive experiments, we validate the robustness of TCReC and its ability to maintain high reconstruction accuracy across varying packet loss rates and datasets, underscoring its importance in edge computing environments where reliable transmission is often compromised by unstable wireless channels. Moreover, TCReC demonstrates strong practical relevance in real-world applications. Within edge computing architectures, it can be deployed on infrastructure such as edge servers, facilitating reliable traffic analysis at the network edge--where data loss frequently arises due to constrained bandwidth and unstable wireless conditions. In the context of cybersecurity, TCReC enhances the detection of malicious activities, such as PortScan and DoS attacks, by effectively preserving the intrinsic characteristics of attack traffic and maintaining clustering patterns consistent with those observed in the original data (Figure 10). While TCReC has demonstrated its effectiveness in reconstructing network traffic characteristics, there still exist opportunities for further enhancement and improvement in certain areas. Firstly, we plan to evaluate TCReC on real IoT network traffic and network simulators to better understand its performance in practical deployment scenarios and its interaction with specific network protocols and hardware constraints. Secondly, we will delve into the distinct characteristics of different traffic types and enhance TCReC's scalability to encompass a broader range of scenarios. Thirdly, we will explore incremental training techniques for TCReC to accommodate the increasing volume of online data samples over time. Lastly, optimizing the execution time of TCReC is essential to meet the demands of real-time analysis scenarios. By addressing these aspects, we aim to enhance TCReC's performance and applicability in network traffic analysis. Conceptualization, J.Y. ; Methodology, J.Y., Y.C. and A.S.; Software, Y.C., A.S. and Y.L. ; Validation, Y.C., Y.L. and A.S.; Resources, J.Y. and Z.L. ; Data curation, J.Y., Y.C., Y.L. and A.S.; Writing--original draft, J.Y., Y.C. and A.S.; Writing--review and editing, J.W., J.Y. and Y.L. ; Visualization, Y.C. and J.Y. ; Supervision, J.W., J.Y. and Z.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Shanghai Science and Technology Project under Grant 23511100500 and Grant 22510761000, and the National Natural Science Foundation of China under Grant 61873309.