DDoS attack detection based on LSTM-GRU in SDN environment

Chenguang Gao; Mingxuan Yin

doi:10.1117/12.3004032

11 October 2023 DDoS attack detection based on LSTM-GRU in SDN environment

Chenguang Gao, Mingxuan Yin

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001V (2023) https://doi.org/10.1117/12.3004032
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

The implementation of Software-Defined Networking (SDN) as a new network architecture brings about a variety of novel characteristics and application scenarios, decreasing network development redundancy, but it also raises serious security concerns. This study describes and develops the recurrent neural network-based LSTM-GRU detection algorithm for Distributed Denial-of-Service attacks (DDoS) assaults in SDN, which uses LSTM and GRU as the base model and stacks them to achieve high accuracy classification of traffic. According to tests, the detection algorithm outperforms conventional detection techniques, achieving accuracy levels of 99.53% and 98.78% on the CIC-IDS 2017 and CIC-DDoS 2019 datasets, respectively.

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

Citation Download Citation

Chenguang Gao and Mingxuan Yin "DDoS attack detection based on LSTM-GRU in SDN environment", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001V (11 October 2023); https://doi.org/10.1117/12.3004032

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