Open AccessA Comprehensive Study of Malware Detection in Android Operating Systems
Author(s) -
Suhaib Jasim Hamdi,
Ibrahim Mahmood Ibrahim,
Naaman Omar,
Omar M. Ahmed,
Zryan Najat Rashid,
Awder Mohammed Ahmed,
Rowaida Khalil Ibrahim,
Shakir Fattah Kak,
Hajar Maseeh Yasin,
Azar Abid Salih
Publication year - 2021
Publication title -
asian journal of research in computer science
Language(s) - English
Resource type - Journals
ISSN - 2581-8260
DOI - 10.9734/ajrcos/2021/v10i430248
Subject(s) - android (operating system) , malware , android malware , computer science , computer security , mobile device , operating system
Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.