Deep learning-based embedded network traffic monitoring techniques

Mingkang Zhang; Yunxi Li; Guangzhe Lv

doi:10.1117/12.3035006

11 July 2024 Deep learning-based embedded network traffic monitoring techniques

Mingkang Zhang, Yunxi Li, Guangzhe Lv

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100N (2024) https://doi.org/10.1117/12.3035006
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

The scale and functionality of modern embedded system software is expanding, and the ponderous network data traffic arises. To ensure the smooth operation of the system, network traffic monitoring is essential. The article studies the application of deep learning technology in the field of network traffic monitoring and analyzes the use of deep learning for traffic classification, traffic prediction, fault management, and security. It proposes to apply deep learning in the field of embedded network traffic monitoring, analyzes the challenges, introduces the existing research results, and looks forward to the new methodology, which is of great significance to deep learning as well as the embedded field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingkang Zhang, Yunxi Li, and Guangzhe Lv "Deep learning-based embedded network traffic monitoring techniques", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100N (11 July 2024); https://doi.org/10.1117/12.3035006

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