Software Vulnerability Detection Tool Using Machine Learning Algorithms

Modern techniques focus on software vulnerabilities because their grave security risks to computer systems present major threats to information protection. Multiple detection methods have emerged to assist in performing code inspections. Research that employs machine learning approaches has demonstrated encouraging detection results among all such methods. This paper evaluates 22 recent vulnerability detection models based on deep learning to understand the application of contemporary neural algorithms for vulnerable code pattern identification. The recent studies have introduced four revolutionary concepts which dramatically influence deep learning vulnerability detection methods and we present detailed explanations of their contributions to this research domain. We analyze the remaining research papers based on the game changers to explain their methods and solutions which either depend on or enhance the game changers and offer our predictions about future research directions. The research examines the obstacles within this domain followed by discussions of forthcoming research areas. The present work intends to inspire more research and investigations within this quickly advancing yet evolving field. Researchers have developed multiple detection methods to assist during code evaluations.