ABSTRACT
In software development model, various software vulnerabilities exist, because of its incredible consideration in current years and its possibly huge level of influence on computer security and information security. In today’s era, several approaches are used to help in code inspection and finding out the existence of vulnerabilities. By use of some particular methods with applicable machine learning concept, very good results can be achieved. This book chapter reviews more than 22 recent technologies that use deep learning mechanisms to analyse vulnerabilities. It points out how they apply state-to-state neural techniques that are helpful for capturing probable vulnerable codes and patterns. It also provides complete reviews of the visions, concepts and ideas that the game modifiers for their field of interest. Six game modifiers are identified using various approaches and solutions that are helpful to build game changers. This chapter identifies various software vulnerabilities and discusses possible exploration directions. It also lists the outcomes of top 10 software vulnerabilities and their detailed description explaining various guidelines on how to avoid software vulnerabilities. Day by day, vulnerability-related records are increasing in every field, and the widespread utility of machine learning methods and software program vulnerability evaluation strategies based on machine learning is turning into an essential research vicinity of statistics security. This book chapter proposes updated and commonplace works in this lookup area that has been analysed very nicely. The main framework according to software program vulnerability evaluation depends on machine learning that is used in the detection methodology.
