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Android application behavioural analysis for data leakage
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DOI：10.1111/exsy.12468.png)
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Abstract
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An android application requires specific permissions from the user to access the system resources and perform required functionalities. Recently, the android market has experienced exponential growth, which leads to malware applications. These applications are purposefully developed by hackers to access private data of the users and adversely affect the application usability. A suitable tool to detect malware is urgently needed, as malware may harm the user. As both malware and clean applications require similar types of permissions, so it becomes a very challenging task to differentiate between them. A novel algorithm is proposed to identify the malware-based applications by probing the permission patterns. The proposed method uses the k-means algorithm to quarantine the malware application by obtaining permission clusters. An efficiency of 90% (approx.) is attained for malicious behaviour, which validates this work. This work substantiates the use of application permissions for potential applications in android malware detection.
Keywords:
android app analysis
android permission
data leakage assessment
machine learning
malware data analysis
reverse engineering
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