Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature Review

Abstract

A large number of state-of-the-art studies on android malware detection and analysis techniques have been published during the last decade. A few comprehensive surveys also exist on the subject. The authors proposed different techniques, tools and frameworks to identify the malware. However, no study attempted to address a systematic review of literature on detection and analysis of android malware methods, systems, and frameworks. In this thesis, we have attempted to present a systematic review of literature on android malware detection and analysis techniques and tools. In this review study, we have identified different android malware detection and analysis methods and tools presented from 2010 to 2021 by following the guidelines of Kitchenham Systematic Literature Review methodology. This thesis presents the 75 most relevant studies out of 3343 published studies. We have determined the android malware datasets used by the android malware detection and analysis techniques. This thesis work has identified that the most used malicious datasets are Genome (39%) and Drebin (36%). We have identified the source code analysis methods in terms of static, dynamic, and hybrid used by android malware detection techniques. We have also identified the limitations and future directions of existing techniques as research gaps for the community. Based on the pragmatic evidence of this research, we have proposed a hybrid analysis based multiple feature analysis framework. This framework will not only address the limitations of static and dynamic based approaches, but it will also analyse evolving android malware datasets using deep neural network and machine learning techniques and improve the accuracy for evolving malware samples