Abstract:
The number of applications for smart mobile
devices is steadily growing with the continuous
increase in the utilization of these devices. the
Installation of malicious applications on smart
devices often arises the security vulnerabilities such
as seizure of personal information or the use of smart
devices in accordance with different purposes by
cyber criminals. Therefore, the number of studies in
order to identify malware for mobile platforms has
increased in recent years. In this study, permissionbased
model is used to detect the malicious
applications on Android which is one of the most
widely used mobile operating system. M0Droid and
DroidScreening data sets have been analyzed using
the Android application package files and
permission-based features extracted from these files.
In our work, permission-based model which applied
previously across different data sets investigated to
M0Droid and DroidScreening datasets and the
experimental results has been expanded. While
obtaining results, feature set analyzed using different
classification techniques. The results show that
permission-based model is successful on M0Droid
and DroidScreening data sets and Random Forests
outperforms another method. When compared to
M0Droid system model, it is obtained much bet ter
conclusions depend on success rate. Our approach
provides a method for automated static code analysis
and malware detection with high accuracy and
reduces smartphone malware analysis time.
