(1.College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025,China; 2.Shanghai Key Laboratory of Information Security Management Technology Research,Shanghai 200240,China)
Abstract： Aiming at the problem of Web attack traffic detection,a convolutional neural network model based on Dynamic Adaptive Pooling Algorithm (DAPA) was proposed.Firstly,each request traffic in the data set is trimmed,aligned,and complemented to generate a series of 50 × 150 matrix data A as input.Then,a dynamic adaptive convolutional neural network model built to detect abnormal traffic can adjust the pooling process dynamically according to different feature maps,and a Dropout layer can be added to the network structure to solve the problem of overfitting in the flow feature extraction process.Experiments show that the method has an accuracy improvement of 1.2%,a loss value of 2.6%,and an overfitting problem is solved compared with the method without using dynamic adaptive pooling.
Key words : abnormal flow detection;convolutional neural network;dynamic adaptive pooling
