MobiPCR: Efficient, accurate, and strict ML-based mobile malware detection

Abstract

Mobile devices have been and will be continuously prevalent as rich applications are provided for various demands. However, the mobile operating system lacks efficient malware detection tools, which puts personal data at risk. This paper presents MobiPCR, a trict, accurate, efficient mobile-oriented malware detection system. MobiPCR basically integrates a (n) (edge) cloud-based architecture, a powerful yet efficient machine learning-based detection model, and a neat detection process. We implemented the MobiPCR prototype system and conducted rigorous experiments to evaluate its performance from different perspectives. We implemented the MobiPCR prototype system on the Android platform (Installer Hooker part) consiering considering that Android is an open-source platform that (i) can be easily modified and (ii) provides rich documentation. We used LineageOS 13 (a widespread Android distribution) to provide the necessary drivers to support communication and the camera for casual usage. Experimental results prove that MobiPCR can strictly and accurately detect malwares and outperform existing similar applications without extra operations.