A Comprehensive Study of Malware Detection in Android Operating Systems
Suhaib Jasim Hamdi *
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Ibrahim Mahmood Ibrahim
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Naaman Omar
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Omar M. Ahmed
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Zryan Najat Rashid
Sulaimani Polytechnic University, Sulaimani, Kurdistan Region, Iraq.
Awder Mohammed Ahmed
Sulaimani Polytechnic University, Sulaimani, Kurdistan Region, Iraq.
Rowaida Khalil Ibrahim
University of Zakho, Duhok, Kurdistan Region, Iraq.
Shakir Fattah Kak
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Hajar Maseeh Yasin
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Azar Abid Salih
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
*Author to whom correspondence should be addressed.
Abstract
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.
Keywords: Malware, detection, operating system, android, viruses