Vulnerability Management using Machine Learning Techniques

Abstract

This paper presents some technologies and methods for Machine learning. It then highlights security concerns, attack vectors, and logical vulnerabilities. To address these security concerns and shortcomings, we present an areas way to deal with intelligent weaknesses. Programming vulnerability are an essential worry in the IT security industry, as malicious programmers who find these vulnerabilities can regularly miss use them for revolting purposes. Web applications will continue to be presented to events that fail to capitalize on their drawbacks until the end of time. In this research, we examine how AI methods may be used to improve the visibility of Web Application Firewalls (WAFs) that are structures used to identify and prevent assaults. We provide the Cybersecurity Vulnerability Ontology (CVO), a decided model for formal data depiction of the board region's weakness, and we use it to construct a Cyber Intelligence Alert (CIA) structure that sends out advanced alerts concerning faults and security measures. There thoroughly assessed the CVO just as the exactness, execution, and helpfulness of the CIA framework.