基于深度学习的DDoS攻击检测模型

DDoS Attack Detection Model Based on Deep Learning

构建了基于粒子群优化卷积神经网络(PSO-CNN)的分布式拒绝服务攻击(DDoS)攻击检测模型.利用卷积神经网络的权值共享和最大池化自动挖掘网络数据流特征,引入粒子群对卷积核进行优化,在提升模型训练效率的同时,增强了模型的全局寻优能力.实验结果表明,该模型能够有效检测DDoS攻击,具有较高的检测准确率.

This study constructs a Distributed Denial-of-Service(DDoS)attack detection model based on Particle Swarm Optimization-Convolutional Neural Network(PSO-CNN).First,it uses the weight sharing and maximum pooling of CNN to automatically mine the features of data streams.Then,it applies PSO to the convolution kernel,thus increasing the training efficiency and enhancing the global optimization.In conclusion,the model proposed in this study has high detection accuracy for DDoS attacks.