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.

中文翻译：

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使用具有性能指标的集成分类方法在线检测恶意软件的可行性

如今，来自云的大多数服务在所有商业，公共和私有区域中都是蓬勃发展的。云计算系统的主要困难是使虚拟化环境不受所有入侵者的侵害。现有系统使用基于签名的方法，无法提供对恶意软件的准确检测。提出了一种基于特征提取和多种分类技术的恶意软件检测方法。最初，将清除干净文件和恶意软件文件。特征选择包括增益比率以提供子集特征。该分类用于预测已输入到移动设备中的任何恶意软件。在本文中，建议使用集成分类器，该分类器包含不同种类的分类器，例如支持向量机，K最近邻，和朴素贝叶斯分类。这些一起被称为元分类器。这三种分类方法已用于拟议工作，并获得了更高的准确性。这可以使用集成度方法以高精度和召回值来衡量发生的预测的正确性，而召回值可以专门确定所使用技术的质量。