Enhancing network traffic classification with CNN and multilayer BiGRU-attention

Haifeng Dong; Yi An

doi:10.1117/12.3051791

18 November 2024 Enhancing network traffic classification with CNN and multilayer BiGRU-attention

Haifeng Dong, Yi An

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032A (2024) https://doi.org/10.1117/12.3051791
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

The rise in network security awareness has led to an exponential increase in the use of encrypted traffic. While encryption protects user privacy, it also presents significant challenges for network security detection. This paper introduces a novel encrypted traffic classification method combining Convolutional Neural Networks (CNN) and multi-layer Bidirectional Gated Recurrent Unit networks (BiGRU). The CNN extracts detailed and global information from traffic data frames, while the temporal attention mechanism in BiGRU captures the temporal relationships between frames. Experimental results indicate that the proposed CNN + BiGRU model achieves over 97.5% accuracy on test sets, outperforming existing deep learning models in both accuracy and F1 score.

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

Citation Download Citation

Haifeng Dong and Yi An "Enhancing network traffic classification with CNN and multilayer BiGRU-attention", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032A (18 November 2024); https://doi.org/10.1117/12.3051791

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KEYWORDS

Feature extraction

Data modeling

Education and training

Deep learning

Machine learning

Statistical modeling

Computer networks

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