Web Application Attack Detection Scheme Using Convolutional Neural Networks

Yeongung Seo, Myungjin Kim, Seungyoung Park, Seokwoo Lee

http://doi.org/10.5626/JOK.2018.45.7.744

Because rates of web application attacks are rapidly increasing, web application attack detection schemes using machine learning have recently become of interest. Existing schemes, however, require the selection of a suitable set of features representing the characteristics of expected attacks, and this set of features needs to be adjusted every time a new type of attack is discovered. In this paper, we propose a web application attack detection scheme employing a convolutional neural network (CNN) without the need to select any features in advance. Specifically, the CNN is trained in a supervised manner with images transformed from hexadecimally converted characters in HTTP traffic, without any restriction in the input characters used. Our experimental results show that the proposed scheme improves detection error rate performance by up to 84.4% over existing schemes.