Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

The ever-increasing growth of online services and smart connectivity of devices have posed the threat of malware
to computer system, android-based smart phones, Internet of Things (IoT)-based systems. The anti-malware
software plays an important role in order to safeguard the system resources, data and information against
these malware attacks. Nowadays, malware writers used advanced techniques like obfuscation, packing,
encoding and encryption to hide the malicious activities. Because of these advanced techniques of malware
evasion, traditional malware detection system unable to detect new variants of malware. Cyber security has
attracted many researchers in the past for designing of Machine Learning (ML) or Deep Learning (DL) based
malware detection models. In this study, we present a comprehensive review of the literature on malware
detection approaches. The overall literature of the malware detection is grouped into three categories such as
review of feature selection (FS) techniques proposed for malware detection, review of ML-based techniques
proposed for malware detection and review of DL-based techniques proposed for malware detection. Based on
literature review, we have identified the shortcoming and research gaps along with some future directives to
design of an efficient malware detection and identification framework.