Enhancing Burp Suite with Machine Learning Extension for Vulnerability Assessment of Web Applications

Abstract

Today’s web represents the most extensive engineered system ever created by humankind. Web security is critical to web application providers and end-users. Burp Suite is established as a state-of-the-art and fully featured set of tools for web vulnerability scanners. This paper presents a novel approach using state of the art Machine Learning algorithms applied to the Burp Suite extension. These algorithms were used to scan for: SQL injection, Cross-Site Request Forgery, and XML External Entity vulnerabilities in university web applications. The results show that the best algorithm is Long Short-Term Memory and that the targeted website is safe to use.

Keywords:

• Burp suite
• logistic regression
• random forest
• gradient boosting
• long short-term memory
• machine learning
• extension
• web vulnerability