Permission based malware detection in android devices
Soussi Ilham, Abderrahim Ghadi, Boudhir Anouar Abdelhakim · 2018
The mobile operation system Android is one of the most OS's used in the entire world, which make it the target of many malware projects and the mission of detecting those malware applications is getting harder over time due to evaluation and development of techniques that make possible for those malwares to hide their maliciousness activities from anti-malware techniques by obfuscating the code source of application or even hiding malicious activities when it's getting to scan by an anti-malware, for this purpose many researchers have paid attention to this subject by proposing different approaches using newest technologies of machine learning and reverse engineering to deal with this problematic. In this paper a permission-based approach is proposed for detecting malwares in android applications using filter feature selection algorithms to select features and machine learning algorithms Random Forest, SVM, J48 for classification of applications into malware or benign.