Android malware detection using permission based static analysis

• Main Authors: Mohd Ariffin, Noor Afiza, Casinto, Hanna Pungo
• Format: Article
• Published: Semarak Ilmu Publishing 2023
• Online Access: http://psasir.upm.edu.my/id/eprint/106547/; https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/2291

The increase of mobile device enhancement grows. With this development, mobile phones are supporting many programs, and everyone takes advantage of them. Nevertheless, malware applications are increasing more and more so that people can come across lots of problems. Android is a mobile operating system that is the most used on smart mobile phones. Because it is the most used and open source, it has been the target of attackers. Android security is related to the permissions allowed by users to the applications. There have been many studies on permission-based Android malware detection. In this study, a permission-based Android malware system is analyzed. Unlike other studies, we propose a permission weight approach. Each of the permissions is given a different score using this approach. Then, K-nearest Neighbor (KNN) and Na¯ve Bayes (NB) algorithms are applied, and the proposed method is compared with the previous studies and the expected experimental results of the proposed approach will be higher.