In order to solve the problem that the accuracy of Slow HTTP Denial of Service (SHDoS) attack traffic detection decreases when the attack frequency changes, a method of SHDoS attack traffic detection method based on one-dimensional Convolutional Neural Network (CNN) was proposed. First, the message sampling and data stream extraction were performed on three types of SHDoS attack traffic under multiple attack frequencies by the method. Then, a data stream conversion algorithm was designed to convert the collected attack data streams into one-dimensional sequences and remove the duplicated sequences. Finally, a one-dimensional CNN was used to construct a classification model. The model was used to extract sequence fragments through the convolution kernels, and the local patterns of attack samples were learned from fragments. Therefore, the model would have the ability to detect attack traffic with multiple attack frequencies. Experimental results show that, compared with the classification models based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM) network respectively, the proposed model has advantages in detection performance on unknown frequency samples, and has the accuracy and precision reached 96.76% and 94.13% respectively on the validation set. It can be seen that the proposed method can meet the needs of detecting SHDoS traffic with different attack frequencies.