Web-Based Attacks Detection Using Deep Learning Techniques: A Comprehensive Review

Abstract

Web applications are utilized extensively by a broad user base, and the services provided by these applications assist enterprises in enhancing the quality of their service operations as well as increasing their revenue or resources. To gain control of web servers, attackers will frequently attempt to modify the web requests that are sent by users from web applications. Attacks that are based on the web can be detected to help avoid the manipulation of web applications. In addition, a variety of research has offered many methods, one of which is artificial intelligence (AI), which is the method that has been utilized the most frequently to identify web-based attacks recently. When it comes to the protection of web applications, anomaly detection techniques used by intrusion prevention systems are preferred.  Deep learning, often known as DL, is going to be covered in this paper as anomaly-based web attack detection methods and machine learning techniques. With the purpose of organizing our selected techniques into a comprehensive framework that encourages future studies, we first explained the most concepts that related to web-based attacks detection, then we moved on to discuss the most prevalent web risks and may provide inherent difficulties for keeping web applications safe.  We classify previous studies on detecting web attacks into two categories: deep learning and machine learning. Lastly, we go over the features of the previously utilized datasets in summary form.