A Systematic Literature Review on the Mobile Malware Detection Methods

With the advent of the 5G network, the number of mobile users has drastically increased. Consequently, the users are much more susceptible to cyber-attacks such as mobile malware. In order to combat mobile malware, recent studies have employed machine learning techniques. This paper revisits existing research on machine learning-based mobile malware detection in cybersecurity. Our study focuses on subjects such as mobile system destruction and information leaks. We explore the mobile malware detection techniques utilized in recent studies based on the attack intentions such as (i) Server, (ii) Network, (iii) Client Software, (iv) Client Hardware, and (v) User. We hope our study can provide future research directions and a framework for a thorough evaluation. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to improved and more practical research.