A SURVEY ON SUPERVISED METHOD FOR DETECTION OF MALWAREJournal: International Journal of Engineering Sciences & Research Technology (IJESRT) (Vol.5, No. 6)
Publication Date: 2016-06-30
Authors : Nisha Badwaik; Vijay Bagadi;
Page : 761-763
Keywords : Android; malicious application; machine learning; discriminative;
The Number of Android mobile devices has been increased in recent year. There are so many approaches for detection of android malware has been proposed by using permission or source code analysis or dynamic analysis. In this survey paper, we use a probabilistic discriminative model for detection of malware by using supervised method .It also show that probabilistic model based on regularized regression also it works well with permission. Furthermore this first research model achieves the best detection result code and application permission. In the previous work limited tool were used for analysis of the APK file, we will be using three of th e most advanced tool for APK analysis namely 1.dex2jar, 2. Java Decompiler, 3.class file analyzer. These three tools will give us complete part of analysis for the existing system.
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Last modified: 2016-06-22 20:52:15