A Detailed Study of Vulnerability Detection using Common Vulnerabilities and Exposures from NVD using Machine Learning and Deep Learning Models

In the recent era, usage and demand of software devices are growing rapidly. Due to cause this, chances of cyberattacks and the vulnerability is also growing at the rapid rate. Therefore, securing this type of sensitive data and detecting types and level of vulnerability is our prime responsibility. Common Vulnerabilities and Exposures (CVE) is one of the popular databases that widely used for predicting and tracking the software as well hardware vulnerability; The types and level of vulnerability can be identified by unique CVE-id number and CVSS Severity respectively. Vulnerability can be detected in the early stage by using machine learning and deep learning models. NVD provides the total number of cyber-attacks with their name that are responsible to damage data. In this paper list of ML and DL models or algorithms are discussed along with the performance of software and their future scope to improve the overall results and detect the risk factor at the initial stage.