Investigation on Challenges and Modern Implications for Android Malware Detection Using Machine Learning

Abstract

Malware or malicious software is a term for various viruses, spyware, or ransom ware that cause harm to a user, their data, or their devices. Android operating system ranks first in the market share due to the system’s smooth handling and many other features that it provides to Android users, which has attracted cyber criminals. Traditional Android malware detection methods, such as signature-based methods or methods monitoring battery consumption, may fail to detect recent malware. The term malware implies malicious intent on the side of the software developer. In recent times very sophisticated and complicated malware are being produced on a regular basis and it has grown into one of the stealthiest and lethal attack techniques used against critical information technology infrastructures. During 2022, the worldwide number of malware attacks reached 5.5 billion, an increase of two percent compared to the preceding year, according to Kaspersky, 80.69% of attacks on mobile users belonged to malware. This paper provides a systematic review of ML-based Android malware detection techniques, enables researchers to acquire in-depth knowledge in the field and to identify potential future research development directions and to understand the current situation with android apps and also provides a few approaches to develop future technologies for android systems.