Detecting DDoS issues under 5G with ResNet

Zhenjiang Liu

doi:10.1117/12.2657423

13 January 2023 Detecting DDoS issues under 5G with ResNet

Zhenjiang Liu

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 1251013 (2023) https://doi.org/10.1117/12.2657423
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

The operation of the network is often accompanied by network attacks, and as the network begins to gradually enter the 5G era, the intensity of network attacks and attacks are also rising significantly, of which Distributed Denial of Service (DDoS), as the mainstream attack of network attacks, has a very high attack intensity and attack frequency, and the traditional detection system is unable to detect the complex DDoS attacks. However, when the detection scheme is combined with artificial intelligence, it becomes efficient and reliable. Over the years, the detection of DDoS attacks has achieved very good results. However, in the face of the advent of the 5G era, higher requirements are put forward for detecting DDoS attacks. Given the good potential and performance of Convolutional Neural Networks (CNN) in classifying images, this work proposes a method to convert network traffic into images and apply the ResNet model for training on the converted data. The work sacrifice some of the accuracy on binary classification, which drops to 99.6% but improve 2% on multivariate classification over previous work to 89%.

Citation Download Citation

Zhenjiang Liu "Detecting DDoS issues under 5G with ResNet", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 1251013 (13 January 2023); https://doi.org/10.1117/12.2657423

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