CBAN: A DDoS detection method based on CNN, BiGRU, and attention mechanism

Bing Wang; Yankun Yu; Chunlan Zhao; Jingjing Jiang

doi:10.1117/12.3032053

6 June 2024 CBAN: A DDoS detection method based on CNN, BiGRU, and attention mechanism

Bing Wang, Yankun Yu, Chunlan Zhao, Jingjing Jiang

Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 131750I (2024) https://doi.org/10.1117/12.3032053
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China

Abstract

In order to address the obstacles posed by the growing security issues of the Internet of Things to the development of big data, this paper conducts in-depth research on the defense of the most harmful DDoS attack. This paper designs a deep learning model for DDoS detection - CBAN. The CBAN model integrates technologies such as 1D-CNN, BiGRU, and attention mechanism for structural design. This model can effectively extract spatial and temporal features of network traffic data for efficient detection of potential DDoS attacks. The CBAN model has shown excellent performance on the CIC-DDoS-2019 dataset.

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

Citation Download Citation

Bing Wang, Yankun Yu, Chunlan Zhao, and Jingjing Jiang "CBAN: A DDoS detection method based on CNN, BiGRU, and attention mechanism", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 131750I (6 June 2024); https://doi.org/10.1117/12.3032053

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