Software vulnerability detection tool using machine learning algorithms

Abstract

There has been a lot of focus on exploitable software vulnerabilities recently because to the seriousness of the damage they may bring to data and computer security. Code inspection has been aided by several suggested vulnerability detection methods. One set of research has shown encouraging outcomes when using machine learning approaches to these strategies. With the goal of demonstrating how these 22 recent research use state-of-the-art neural approaches to identify potential problematic code patterns, this article covers deep learning as a vulnerability detection method. From the papers we looked at, we were able to pick out four that really changed the game when it came to using deep learning for vulnerability identification. We also gave you the lowdown on what these four studies had to say about the field as a whole. Reviewing the remaining studies in light of the four game-changers, we offer their methods and solutions, which either expand upon or build upon the game-changers, and we share our thoughts on the trends that will shape future research. We also talk about possible areas for future study and point out the difficulties encountered in this area. We want to inspire readers to delve more into this emerging yet rapidly expanding field of study.