GBERT: a hybrid BERT and GRU model for enhanced encrypted traffic classification

Yucheng Zhou; Wenjun Yang

doi:10.1117/12.3038155

22 August 2024 GBERT: a hybrid BERT and GRU model for enhanced encrypted traffic classification

Yucheng Zhou, Wenjun Yang

Proceedings Volume 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024); 132281L (2024) https://doi.org/10.1117/12.3038155
Event: Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 2024, Guangzhou, China

Abstract

As encrypted traffic becomes increasingly prevalent in modern network communications, effective classification of encrypted traffic is crucial for network security and management. However, existing classification methods face significant challenges when dealing with encrypted traffic, especially in capturing the complex temporal features and contextual information of traffic data. For this reason, this study proposes the GBERT model, a novel hybrid neural network structure that integrates the contextual understanding capabilities of the BERT model with the temporal processing advantages of Gated Recurrent Unit (GRU). Validated across several public encrypted traffic datasets, the GBERT model demonstrates exceptional performance, notably achieving classification accuracies of 99.03% on the ISCX-VPN-Service dataset and 99.32% on the USTC-TFC dataset. Additionally, this study explores various GRU configurations, offering valuable insights for selecting the most effective model architecture for specific tasks.

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

Citation Download Citation

Yucheng Zhou and Wenjun Yang "GBERT: a hybrid BERT and GRU model for enhanced encrypted traffic classification", Proc. SPIE 13228, Fifth International Conference on Computer Communication and Network Security (CCNS 2024), 132281L (22 August 2024); https://doi.org/10.1117/12.3038155

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