Paper Title
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.