Abstract:
The increasing popularity of smartphones and
tablets has introduced Android malware which is
rapidly becoming a potential threat to users. A recent
report indicates the alarming growth rate of Android
malware in which a new malware is introduced in
every second more precisely in 10 seconds. To against
this dangerous malware growth, this paper proposes
a scalable malware detection system using permission
analysis behavior that can identify malware apps
effectively and efficiently. We propose multi-level of
pruning procedures to identify the most significant
permission instead of extracting all permissions. The
propose system utilizes supervised classification
method in machine-learning to classify different
families of benign and malware apps. We found that
22 permissions are significant actually. Our
evaluation finds that the analysis time of using these
22 permissions are 4 to 32 times less than using all
permissions. The results show that most of malware
apps are located the unnecessary permission on
AndroidManifest.xml to inject the malicious codes in
the apps.
