Online malware detection using ensemble classification method with performance metrics

Abstract

newline 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.PDF files are considered to be safe for the static file format. On the other hand, PDF files can be executed using various codes as a malware. Previous researches have conducted to find the PDF format used as a malware; have not extracted the features of PDF. To overcome the problem of cloud computing and PDF files, the following methods are proposed. Online Malware Detection in Cloud using Ensemble Classification Model with Performance Metrics Automatic Malware Detection in PDF Files using Enhanced Classification Model The first method 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 research work, 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. newlineIn the second method, the existing decision tree enhanced with some optimal function called enhanced decision tree (EDT). Two stages are used in the second work such as training stage and testing stage. In the training stage, the features are extracted from given PDF files with the