Abstract: Nowadays, Android-based devices are more utilized than other Operating Systems based devices. Statistics show that the market share for android on mobile devices in March 2018 is 84.8 percent as compared with only 15.1 percent iOS. These numbers indicate that most of the attacks are subjected to Android devices. In addition, most people are keeping their confidential information on their mobile phones, and hence there is a need to secure this operating system against harmful attacks. Detecting malicious applications in the Android market is becoming a very complex procedure. This is because as the attacks are increasing, the complexity of feature selection and classification techniques are growing. There are a lot of solutions on how to detect malicious applications on the Android platform but these solutions are inefficient to handle the features extraction and classification due to many false alarms. In this work, the researchers proposed a multi-level permission extraction framework for malware detection in an Android device. The framework uses a permission extraction approach to label malicious applications by analyzing permissions and it is capable of handling a large number of applications while keeping the performance metrics optimized. A static analysis method was employed in this work. Support Vector Machine (SVM) and Decision Tree Algorithm was used for the classification. The results show that while increasing input data, the model tries to keep detection accuracy at an acceptable level.

Ali Ghasempour, Nor Fazlida Mohd Sani and Ovye John Abari, “Permission Extraction Framework for Android Malware Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111159

@article{Ghasempour2020,
title = {Permission Extraction Framework for Android Malware Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111159},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111159},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Ali Ghasempour and Nor Fazlida Mohd Sani and Ovye John Abari}
}