Prevention of SQL injection based on artificial neural network

Abstract

" Day by day the usage of enterprise web applications have been increased. Therefore, almost all of hackers try to acquire the details of the users, the payment methods, numbers, the other methods which they are connected with the people and inter-connected with day-to day activities and etc…There is a high possibility and a risk to expose user data and the privacy. This is known as the Web Application Vulnerability. In this web application vulnerability, SQL injection is a one of the major problem. Most of the web application developers are using traditional validation methods in web application input fields such as regex, firewalls, input sanitizations. These types of methods are no longer effective or appropriate. Because SQL injection techniques are changed from time to time and that is the risk of bypassing variants. This proposed solution identify the SQL injection from the web application input forms and proposed a SQL injection validation model based on the Convolutional Neural Networks. To deal with the web application vulnerability, advantage of high dimensional features of SQL injection behavior can be taken. The proposed approach is tested using real web application input form, which is the representative input form validation using regex, input sanitization, and firewall method. The proposed model research findings indicate that the CNN based model has a higher percentage of accuracy, recall, precision, and F1 score, so, it is more accurate to validating attack than traditional methods. This research proves that the vulnerabilities that happened in a web application can be prevented. Advanced technologies have been used and those will help the developer to avoid the SQL injections in a proper and secured way. There is less possibility of hacking using the SQL injection due to this CNN model. So, the hackers cannot reach systems and the data using SQL injection."