DDoS attach detection technology based on CNN and transformer

Pan Wang; Xianghua Miao

doi:10.1117/12.3010685

20 October 2023 DDoS attach detection technology based on CNN and transformer

Pan Wang, Xianghua Miao

Proceedings Volume 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023); 128140Q (2023) https://doi.org/10.1117/12.3010685
Event: Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 2023, Chongqing, China

Abstract

In view of the problems of high data dimension, redundant features and low detection accuracy in current DDoS attack detection, This paper proposes a DDoS attack detection model CNN-Trans based on the combination of Convolutional Neural Network (CNN) and Transformer. The detection model firstly uses CNN integrated with CBAM attention for feature extraction. The model can better learn the depth characteristics of the input data, and then use Transformer to train the processed data to get classification results. Experimental results show that the accuracy and other aspects of the proposed model CNN-Trans have been improved

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pan Wang and Xianghua Miao "DDoS attach detection technology based on CNN and transformer", Proc. SPIE 12814, Third International Conference on Green Communication, Network, and Internet of Things (CNIoT 2023), 128140Q (20 October 2023); https://doi.org/10.1117/12.3010685

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