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Hybrid Framework for Detecting Malicious Apps in Android

Author(s):

Aniruddh Bhilvare , L. D. College Of Engineering; Prof. Trupti Manik, L. D. College Of Engineering

Keywords:

Android, Mobile security, Malware analysis

Abstract

Mobile malware has been growing in scale and complexity due to popularity of smartphones worldwide. Android is one of the most popular mobile operating system, which makes it more suitable to target with malware apps. There are schemes are provided for static malware analysis based on permission based analysis or dynamic malware analysis based on system calls. Despite current detection measures in place, more robust solution for malware detection is required. We have here described a hybrid study for the detection of malicious applications. In our study, we have suggested combination of static and dynamic malware analysis techniques to detect Android malware applications. Permission based filtering techniques can be effective to uncover well-known malwares. Then we can apply dynamic analysis scheme to identify certain behaviors of obfuscated malwares. In our scheme, we are monitoring dynamic behavior of application to uncover their malicious behavior along with static analysis consisting of reverse engineering of Apps. By combining both static and dynamic behavior, we can device a powerful framework to detect malicious apps on Android platform.

Other Details

Paper ID: IJSRDV3I40790
Published in: Volume : 3, Issue : 4
Publication Date: 01/07/2015
Page(s): 1333-1336

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