Enhanced OpenVAS for Analysis of Vulnerability Assessment in the Cloud Environments

Abstract

This research tackles the limitations of  OpenVAS within a network system in effectively assessing vulnerabilities in cloud environments, which pose unique and complex challenges compared to conventional networks. The study's objectives includes enhancing the configuration and integration of OpenVAS with cloud-native security tools to improve accuracy and efficiency, testing the effectiveness of the enhanced OpenVAS in identifying and classifying cloud-based vulnerabilities, and comparing its performance of the OpenVAS within a network environments. The research methodology employs a virtualized environment using Virtual Machines (VMs), Metasploitable 2 as the vulnerable target, and Kali Linux for penetration testing. The enhanced OpenVAS was then tested in controlled cloud settings to evaluate its severity rate classification, scan time, and detection rate. Results indicated significant improvements such as the severity classification which was more accurate, scan times were reduced by approximately 30%, and the detection rate was enhanced, identifying previously overlooked vulnerabilities unique to cloud infrastructures. Major findings underscore that the integration of cloud-native tools and intelligence significantly bolsters the performance of network environments vulnerability assessment. This research contributes to the cybersecurity field by offering a refined vulnerability assessment framework capable of adapting to the evolving threat landscape in cloud environments. It provides practical insights for cybersecurity professionals, enabling more effective and efficient protection of cloud-based assets and ensuring better resilience against sophisticated digital threats.