Empirical study on intelligent android malware detection based on supervised machine learning

• Main Authors: Abdullah, Talal A.A., Ali, Waleed, Abdulghafor, Rawad Abdulkhaleq Abdulmolla
• Format: Article
• Language: English; English
• Published: Science and Information Organization 2020
• Subjects: T10.5 Communication of technical information
• Online Access: http://irep.iium.edu.my/84592/20/84592%20Empirical%20Study%20on%20Intelligent%20Android%20Malware.pdf; http://irep.iium.edu.my/84592/8/84592_Empirical%20Study%20on%20Intelligent%20Android%20Malware%20Detection_SCOPUS.pdf; http://irep.iium.edu.my/84592/; https://thesai.org/Downloads/Volume11No4/Paper_29-Empirical_Study_on_Intelligent_Android_Malware.pdf
• Institution: Universiti Islam Antarabangsa Malaysia

The increasing number of mobile devices using the Android operating system in the market makes these devices the first target for malicious applications. In recent years, several Android malware applications were developed to perform certain illegitimate activities and harmful actions on mobile devices. In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications. However, the most recent Android malware apps, such as zero-day, cannot be detected through conventional methods that are still based on fixed signatures or identifiers. Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing information to detect the new Android malware apps. This paper presents the basic concepts of Android architecture, Android malware, and permission features utilized as effective malware predictors. Furthermore, a comprehensive review of the existing static, dynamic, and hybrid Android malware detection approaches is presented in this study. More significantly, this paper empirically discusses and compares the performances of six supervised machine learning algorithms, known as K-Nearest Neighbors (K-NN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and Logistic Regression (LR), which are commonly used in the literature for detecting malware apps.