Systematic Review on Various Techniques of Android Malware Detection

Smartphone has become the 4th basic necessity of human being after Food, Cloths and Home. It has become an integral part of the life that most of the business and office work can be operated by mobile phone and the demand for online classes demand for all class of students have become a compulsion without any alternate due to the COVID-19 pandemic. Android is considered as the most prevailing and used operating system for the mobile phone on this planet and for the same reason it is the most targeted mobile operating system by the hackers. Android malware has been increasing every quarter and every year. An android malware is installed and executed on the smartphones quietly without any indication and user's acceptance, that possess threats to the consumer's personal and/or classified information stored. To address these threats, varieties of techniques have been proposed by the researchers like Static, Dynamic and Hybrid. In this paper a systematic review has been carried out on the relevant studies from 2017 to 2020. Assessment of the malware detection capabilities of various techniques used by different researchers has been carried out with comparison of the performance of different machine learning models for the detection of android malwares by assessing the results of empirical evidences such as datasets, features, tools, etc. However the android malware detection still faces several challenges and the possible solution with some novel approach or technique to improve the detection capabilities is discussed in the discussion and conclusion.