Group Decision-Making Models for Selection of Virtual Machine Software for Malware Detection Purposes

Abstract

The rapid development of ICT technologies, together with applications, has led to a huge amount of data exchanged in the Internet space. The protection of this data, used both by individual households and by business and scientific organizations, appears to be essential. To be able to protect huge amounts of data against malware attacks, researchers are to be able to understand the malware mechanism to propose adequate measures. For this purpose, proper virtual machine software that is at the core of research efforts for malware detection is to used. Due to the virtualization, multiple OS instances on a single physical machine could be simulated to detect and analysis of malware. In this regard, the selection of appropriate virtual machine software is of great importance, and in the current article, two group decision-making models are proposed. These models were applied in the selection of VM software for desktop Windows deployment. The obtained results demonstrated the applicability of both models.