DDoS attack detection based on LSTM-GRU in SDN environment
Chenguang Gao, Mingxuan Yin · 2023
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.