Abstract: At present,in the field of Web malicious requests detection technology based on convolutional neural network,malicious requests are detected only for the URL part,and each research has different digital representation methods for the original data,which will result in low detection efficiency and detection accuracy.In order to improve the performance of the convolutional neural network in web malicious request detection,this paper introduced other HTTP request parameters to be merged with URLs,and used the dataset HTTP data set CSIC 2010 and DEV_ACCESS as the raw data.The comparative experiment first used six digital representation methods to represent the raw input of the string format,and then put them to the designed convolutional neural network to obtain six different models.At the same time,the classical algorithms HMM,SVM and RNN were trained on the same training data set to obtain the control models.Finally,the nine models were evaluated on the same test data set.The experimental results show that in the multi-parameter Web malicious request detection method,the convolutional neural network using the combination of the vocabulary mapping and the internal embedding layer to represent the original data achieves 99.87% accuracy and 98.92.% F1 score,therefore,the accuracy is improved by 0.4~7.7 percentage points and the F1 value is improved by 0.3~13 percentage points.The experiment fully demonstrate that the multi-parameter Web malicious request detection technology based on convolutional neural network has obvious advantages,and using the vocabulary mapping and the internal embedding layer of the network to represent the original data can make the model achieve the best detection performance.

Key words: Convolutional neural network, Deep learning, Malicious Web request detection, Web security