A novel architecture for web-based attack detection using convolutional neural network

Abstract

Unprotected Web applications are vulnerable places for hackers to attack an organization's network. Statistics show that 42% of Web applications are exposed to threats and hackers. Web requests that Web users request from Web applications are manipulated by hackers to control Web servers. Web queries are detected to prevent manipulations of hacker's attacks. Web attack detection is extremely essential in information distribution over the past decades. Anomaly methods based on machine learning are preferred in the Web application security. This present study aimed to propose an anomaly-based Web attack detection architecture in a Web application using deep learning methods. The architecture structure consists of data preprocess and Convolution Neural Network (CNN) steps. To prove the suitability and success of the proposed CNN architecture, CSIC2010v2 datasets were used. The proposed architecture performed detection of Web attacks, using anomaly-based detection type. Based on the experimental results of the study, the proposed CNN deep learning architecture presented successful outcomes.