SDN Attack Identification Model Based on CNN Algorithm
Huimin Xue, Bing Jing · IEEE Access · 2023
As the complexity of the network structure increases, so do the requirements for the network architecture are also increasing, and Software Defined Network (SDN) technology has emerged. SDN technology has successfully simplified network management, but its open programming nature poses a risk of network attacks. In complex network environments, the recognition accuracy of traditional recognition models cannot meet the requirements of accuracy and speed. In view of this, this study proposes an attack recognition model based on Convolutional Neural Network (CNN), aiming to solve the attack recognition problems faced in SDN environments, improve the accuracy of the model, and ensure the security of SDN. The study used the NSL-KDD dataset and the MIT LL DARPA dataset. In the performance testing experiment of the model, the results show that the proposed model has an accuracy of 98.25% in SDN attack recognition, and its performance is significantly better than traditional CNN models. The accuracy of traditional attack recognition reaches 98.25%, and its performance is superior to the KNN-PSO model. Verifying its superiority and further confirming its application value in SDN attack identification.