Abstract
Due to the daily use of web applications in several critical domains such as banking and online shopping, cybersecurity has become a challenge. Recently, deep learning techniques have achieved promising results and attracted cybersecurity researchers. In this paper, we explore and evaluate deep learning techniques used for the security of web applications. We analyze through experiments the different factors influencing the performance of the Convolutional Neural Network (CNN) technique for web attacks detection. The experiments done in this paper focus on CNN and have three goals. First, we evaluate the performance of different CNN models using two different methods of data input presentation and data input splitting. Second, we study the impact of the different CNN hyper-parameters on the attack detection rate. Third, we select the best deep learning toolbox that will be used in our future proposed detection technique. Through the experiments conducted in this paper, we reveal that an adequate tuning of hyper-parameters and the way of pre-processing data input have a significant impact on the attack detection rate.