CSKH-02.2020. Abstract—This paper proposes and develops a web attack detection model that combines a clustering algorithm and a multi-branch convolutional neural network (CNN). The original feature set was clustered into clusters of similar features. Each cluster of similar features was generalized in a convolutional structure of a branch of the CNN. The component feature vectors are assembled into a synthetic feature vector and included in a fully connected layer for classification. Using K-fold cross-validation, the accuracy of the proposed method 98.8%, F1-score is 98.9% and the improvement rate of accuracy is 1.479%.