Mobile Malware Detection Using Classfication Techniques
The number of applications for smart mobile devices is steadily growing with the continuous increase in the
utilization of these devices. Security vulnerabilities such as seizure of personal information or the use of smart devices in
accordance with different purposes by cyber criminals often arises through the installation of malicious applications on smart
devices. Therefore, the number of studies in order to identify malware for mobile platforms has increased in recent years. In
this study, permission-based model is used to detect the malicious applications on Android which is one of the most widely
used mobile operating system. M0Droid data set has 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 for M0Droid data set and the experimental results has been expanded. While obtaining
results, feature set analyzed using different classification techniques. The results shows that permission-based model is
successful on M0Droid data set and Random Forests outperforms another methods. When compared to M0Droid system
model, it is obtained much better conclusions depend on success rate.
Keywords- Mobile Malware Detection, Permission data, Classification techniques, M0Droid.