APPLICATIONS OF MACHINE LEARNING IN SOFTWARE DEVELOPMENT TO PREVENT CSRF VULNERABILITIES IN WEB APPLICATIONS

Chavez Quiroz Gabriela Guadalupe

This article dives into the application of Machine Learning (ML) in detecting Server-Side Request Forgery (SSRF) vulnerabilities in web applications. SSRF vulnerabilities present significant risks by allowing attackers to manipulate requests from the server, which can lead to the exposure of sensitive data or unauthorized intrusions. This article examines how ML has become an essential tool for addressing SSRF, highlighting examples of ML algorithms used to identify patterns and anomalies in web requests. In addition, the successful integration of ML-based solutions into the software development cycle is discussed, enabling early and effective detection of SSRF vulnerabilities. In an increasingly interconnected digital world, this approach is crucial to strengthening security in web applications and online systems.

Keywords:Machine Learning, Vulnerability Detection, SSRF, Web Application Security, Cybersecurity