A Network Traffic Characteristics Reconstruction Method for Mitigating the Impact of Packet Loss in Edge Computing Scenarios

Jiawei Ye (), Yanting Chen, Aierpanjiang Simayi, Yu Liu, Zhihui Lu and Jie Wu ()
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Jiawei Ye: School of Computer Science, Fudan University, Shanghai 200433, China
Yanting Chen: School of Computer Science, Fudan University, Shanghai 200433, China
Aierpanjiang Simayi: School of Computer Science, Fudan University, Shanghai 200433, China
Yu Liu: School of Computer Science, Fudan University, Shanghai 200433, China
Zhihui Lu: School of Computer Science, Fudan University, Shanghai 200433, China
Jie Wu: School of Computer Science, Fudan University, Shanghai 200433, China

Future Internet, 2025, vol. 17, issue 5, 1-20

Abstract: This paper presents TCReC, an innovative model designed for reconstructing network traffic characteristics in the presence of packet loss. With the rapid expansion of wireless networks driven by edge computing, IoT, and 5G technologies, challenges such as transmission instability, channel competition, and environmental interference have led to significant packet loss rates, adversely impacting deep learning-based network traffic analysis tasks. To address this issue, TCReC leverages masked autoencoder techniques to reconstruct missing traffic features, ensuring reliable input for downstream tasks in edge computing scenarios. Experimental results demonstrate that TCReC maintains detection model accuracy within 10% of the original data, even under packet loss rates as high as 70%. For instance, on the ISCX-VPN-2016 dataset, TCReC achieves a Reconstruction Ability Index (RAI) of 94.02%, while on the CIC-IDS-2017 dataset, it achieves an RAI of 94.99% when combined with LSTM, significantly outperforming other methods such as Transformer, KNN, and RNN. Additionally, TCReC exhibits robustness across various packet loss scenarios, consistently delivering high-quality feature reconstruction for both attack traffic and common Internet application data. TCReC provides a robust solution for network traffic analysis in high-loss edge computing scenarios, offering practical value for real-world deployment.

Keywords: edge computing; network traffic analysis; packet loss; network traffic characteristic reconstruction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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