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.