Abstract

In the era of Internet of Things (IoT), enterprise information Systems (IISs) are becoming increasingly valuable in a range of industries due to the fact that they constitute a network in which connected devices exchange data in an environment that is quite close to real time. In this context, enterprises are provided with the opportunity to make use of virus detection solutions that are either static, dynamic, or hybrid. The research uses ensemble machine learning approaches that have been implemented and are analyzed, and comparisons are drawn between them. The findings of this research have been effective in the identification of malwares in IIS.