Malware Analysis on Android Apps: A Permission-Based Approach

Abstract

The use of Android devices nowadays is almost inevitable. Having been able to get a big slice of the mobile operating systems, Android has become a wide target for malware attacks. Malware detection analysis in this study is done to contribute to the many various ways in doing the malware analysis using classification algorithm using Random Forest and Naive Bayesian. This study used a static method of analyzing and detecting malware applications through the permission requests made by each Android application as analyzed by Virus Total website. This study utilized fifty actual Android samples downloaded from the Internet in which the samples were composed of twenty-five benign apps and twenty-five malware applications.

Dynamic analysis of malwarae intrusion in mobile devices using Adaboost Algorithm, KNN and SVM base classifiers

“…Mobile devices have penetrated the world's economy in almost all fields, especially in e-commerce, social media, medicine, communication, education, among others. Their ubiquity has endeared them to many users including malware developers and hackers who exploit them for their nefarious activities such as blackmail and arm-twist users to grant dangerous permissions, distort operations and steal information and personal data to make money (Atkinson, 2015;Gamao, 2018). Other motives behind malware attacks include espionage, ideology (terrorism) and fun (Verizon, 2018;GSMA, 2019).…”