Detecting Vulnerabilities Through the Examination of Software in Cloud using Machine Learning Techniques

Abstract

This research proposes a vulnerability prediction approach that analyzes functions/methods/classes in software systems using static analysis and machine learning models. The proposed approach outperformed other vulnerability prediction approaches in publicly available datasets, providing valuable insights to prioritize vulnerability remediation efforts. This approach has the potential to improve software security and help software development teams develop more secure software systems.