An Efficient Machine Learning-based Approach for Android v.11 Ransomware Detection

Abstract

Android ransomware is a threatening malware that is targeting individuals and enterprises. Many existing approaches suggested different ransomware detection solutions to protect users’ devices and data. These solutions used mainly static-based or dynamic-based analysis systems. However, the current solutions have considered only the old versions of Android platforms. In this paper, an efficient machine learning-based ransomware detection approach is proposed. This approach has studied deeply the latest version of Android (Version 11, API Level 30) to include the updated list of features including permissions and API packages calls that might be utilized by ransomware attacks. A new dataset was created after parsing 1000 apps to extract these features. Afterwards, different machine learning techniques were executed to generate different predictive models for Andoird ransomware. Some predictive models reached 98.3% of detection accuracy even after reducing around 26% of the overall features set.