Evaluation of EWCDMCC Cloud Worm Detection Classification Based on Statistical Analysis

Abstract

Cloud computing is defined as a technology that consists of a large number of physical computers connected by using the Internet or it is a distributed computing technology over the network. A large resources, database, applications, services and software are an essential part of this technology. Cloud computing services could be interrupted by malicious codes attack. Worm is one of the most hazardous malicious codes that can damage the cloud services, applications or virtual network infrastructure. Worms attack is now more complex and intelligent and more difficult to be detected and prevented. This paper presents the relationships between features of worm cloud computing classification (EWCDMCC) by using statistical analysis. Prior forming the worm classification, the worm cloud architecture has been studied. As for the static analysis, symmetric measure and chi-square tests were conducted to find the relationship among the features of the proposed classification. The results from chi-square tests show how features are related to each other for initiating worm attacks in the cloud. Subsequently, symmetric measure quantifies the strength of the relationship. � 2017 American Scientific Publishers All rights reserved.