中圖分類號(hào)：TP393
文獻(xiàn)標(biāo)識(shí)碼：A
DOI：10.19358/j.issn.2096-5133.2020.02.002
引用格式：高勝花，李世明，李秋月，等.基于DAPA的卷積神經(jīng)網(wǎng)絡(luò)Web異常流量檢測(cè)方法［J］.信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（02）：8-12.

A convolutional neural network Web abnormal flow detection method based on DAPA

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 overfitting 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 overfitting problem is solved compared with the method without using dynamic adaptive pooling.

Key words : abnormal flow detection;convolutional neural network;dynamic adaptive pooling