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Mobile phone industry is growing at rapid speed .These mobile phones are running on diiferent platform such as JAVA, Android, IOS, Sysmbian and others. Out all these platforms Android cover maximum share amount Smartphone platform. Android platforms supports millions of applications that can be download from various repositories such as google play. These applications are installed and used. The applications present in these repository may be malicious which leady to security problems using  these application. In this paper an effective approach has been proposed for detection of  the malicious application based on the permission groups. In proposed work, binary classification of applications are carried out into two label i.e. Benign and malicious one. In this developed approach the distinguished features are evaluated and filtered out using features evaluation technique such as Information gain, Gain ratio, Gini Index, Chi-square test. Finally based on the features evaluated the classification is done using supervised machine learning techniques

MACHINE LEARNING APPROACH TO DETECT ANDROID MALWARES