A SQL Injection Attack Recognition Model Based on 1D Convolutional Neural Network

The rapid development of Internet technology and the widespread popularization of web applications have brought a lot of convenience to people’s lives. At the same time, due to the interaction of more user information between browsers and servers, attacks on web applications have intensified, and network security issues occur frequently. SQL injection attacks have always been a common tactic of cyber attackers due to their simplicity and high threat level. Nowadays, SQL injection attacks are emerging in an endless stream, and detection models based on traditional machine learning algorithms have been unable to identify effectively and accurately complex SQL injection attacks. In this paper, we propose a SQL injection recognition model based on one-dimensional convolutional neural network (1D CNN) and combining word embedding and ASCII transcoding techniques. The model can recognize all sorts of SQL injection attacks more efficiently and accurately, dramatically lower human intervention, and offer some defense against 0day attacks that never occur.