A High-performance Web Attack Detection Method based on CNN-GRU Model

WEB attack detection is an important part of WEB security. This paper proposes a web attack detection method based on Convolutional Neural Network (CNN) combined with Gated Recurrent Unit (GRU). In order to improve the detection performance, we extract eight statistical features with good classifica...

Full description

Saved in:
Bibliographic Details
Published in2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) Vol. 1; pp. 804 - 808
Main Authors Niu, Qiangqiang, Li, Xiaoyong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2020
Subjects
Analytical models
Automation
CNN
CNN-GRU
Conferences
Convolutional neural networks
data augmentation
Deep learning
Feature extraction
GRU
Logic gates
statistical features
web attack detection
Online AccessGet full text

Cover

More Information
Summary:WEB attack detection is an important part of WEB security. This paper proposes a web attack detection method based on Convolutional Neural Network (CNN) combined with Gated Recurrent Unit (GRU). In order to improve the detection performance, we extract eight statistical features with good classification effect to augment the original data. In addition, we also pre-trained the word embedding matrix using the word2Vec model, then obtained the input of the CNN-GRU model and classified the final results. The experimental results show that the accuracy of the method in the HTTP CSIC 2010 dataset is 99.00%, the recall rate is 97.74%, the F1 value is 98.77% and the precision is 99.82%. Compared with traditional machine learning methods, this method proposed in this paper has better performance.
DOI:10.1109/ITNEC48623.2020.9085028