Score level prediction of Google Chrome Vulnerability using Bayesian Network

Abstract

Abstract. Software vulnerability is a weakness and bug that can affect security protocols of the system. The issue at hand has produced an increased software vulnerability within each lifecycle release. A web browser is used by users as the primary method to communicate with each other online via devices. People can find great content on the web. Challenges arise within security protocols when attackers access users via web browsers. Therefore, web browsers have become a target for attackers who use web browser vulnerabilities. This research focuses on Google Chrome vulnerabilities, and proposes a new approach which can provide probabilistic predictions that generate a score level of the Chrome browser vulnerability. The predication model uses artificial intelligence from the "Bayesian Network ".