MACHINE LEARNING FOR SOFTWARE SECURITY TESTING.

The incorporation of machine learning (ML) into software security testing has become a crucial advancement due to the increasing prevalence of sophisticated cyber-attacks. This integration offers substantial improvements compared to conventional security methods. This article provides a thorough analysis of machine learning methods used to enhance the detection, classification, and management of software vulnerabilities. We explore diverse machine learning techniques and assess their efficacy in automating and improving security operations in various cybersecurity scenarios. The research focuses on analysing the achievements and difficulties faced while training machine learning models for tasks like identifying vulnerabilities and optimizing security controls. This analysis is based on empirical data obtained from current case studies. In addition, we assess crucial areas that require enhancement and suggest strategic approaches for future study with the goal of maximizing the influence of machine learning on software security. This work contributes by doing a thorough investigation of the role of machine learning (ML) in cybersecurity. It presents a convincing argument for the incorporation of ML into security measures to improve their effectiveness. [ABSTRACT FROM AUTHOR]