On viability of detecting malwares online using ensemble classification method with performance metrics

Nowadays, most of the services from cloud are protuberant within the all commercial, public, and private areas. A primary difficulty of cloud computing system is making a virtualized environment safe from all intruders. The existing system uses signature-based methods, which cannot provide accurate detection of malware. This paper put forward an approach to detect the malware by using the approach based on feature extraction and various classification techniques. Initially the clean files and malware files are extracted. The feature selection includes gain ratio to provide subset features. The classification is used to predict any malware that has been entered in the mobile device. In this paper, it is proposed to use the ensemble classifier which contains different kinds of classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes classification. These together are known as a meta classifier. These three classification methods had been used for proposed work and get the results with higher accuracy. This measures the correctness of the prediction happened using ensemble method with high precision and recall values which is specifically identifies the quality of the techniques used.