Single link industrial control network traffic prediction based on flow decomposing

Hao Yang; Xiaoyu Zeng; Ruixia Lang; Jin Wang

doi:10.1117/12.3034808

11 July 2024 Single link industrial control network traffic prediction based on flow decomposing

Hao Yang, Xiaoyu Zeng, Ruixia Lang, Jin Wang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321002 (2024) https://doi.org/10.1117/12.3034808
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

In the industrial control domain, accurate network traffic prediction is crucial for optimizing resource configuration and enhancing system stability. Addressing the limitations of existing methods in handling both nonlinear long-term dependencies and short-term linear features simultaneously, our proposed model adopts a two-fold approach. First, we decompose the traffic sequence into distinct components, each characterized by specific features. For nonlinear dependencies, we employ the Bi-LSTM model, capturing bidirectional relationships and incorporating a self-attention mechanism for adaptive feature weight adjustments. Simultaneously, for linear features, we leverage the NeuralProphet model. In summary, our model outperforms traditional time series prediction methods, demonstrating improved accuracy in forecasting overall network traffic and offering enhanced precision in predicting traffic burst modes and troughs.

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

Citation Download Citation

Hao Yang, Xiaoyu Zeng, Ruixia Lang, and Jin Wang "Single link industrial control network traffic prediction based on flow decomposing", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321002 (11 July 2024); https://doi.org/10.1117/12.3034808

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