A Machine Learning Approach for Web Application Vulnerability Detection Using Random Forest
Chandan Singh, V. Vijayalakshmi, Harsh Raj · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: Website attacks have been one of the main threats to websites and web portals of private and public organizations. In today's digital world web applications are an important part of day-to-day life so it has become a challenging task to secure the applications. The attackers aim to extract sensitive information about the users through the URL links sent to the victims. We are trying filling the gap of traditional methods to stop the attacks, but the traditional methods fail to perform well as the attackers are becoming good at attacking the web applications. People are presently searching for reliable and consistent web application attack detection software. This model aims to secure web applications of vulnerabilities and from different types of attacks using a machine learning approach which has more accuracy compared to other machine learning algorithms since we are using Random Forest Model.