Android malware analysis and detection: A systematic review

Abstract

AbstractAndroid malware has been emerged as a significant threat, which includes exposure of confidential information, misrepresentation of facts and execution of applications without the knowledge of the users. Malware analysis plays an essential role in dealing with the unlawful behaviour of such malicious applications. Android malware analysis involves examining and understanding malware behaviour and its characteristics. It also includes potential adversarial impacts on Android devices. This paper presents a quick understanding and a holistic view of malware detection and analysis. The current investigation conducted a systematic literature review (SLR) to recognize the salient shifts in malware detection by examining a range of scholarly journals and conference papers. The SLR investigated 99 articles published between the years 2018 and 2023. The key observation of this SLR is that static analysis is the most implemented approach for detecting Android malware; Apktool and Androguard are the most frequently used tools. This study also conceded that deep learning and machine learning models have more potential to analyse the malicious behaviour of malware. Certain challenges are faced in Android malware analysis, that is, obfuscation techniques, dynamic code loading, and issues related to experimented datasets. Further, this study focuses on the following areas: the definition of the sample set, data optimisation and processing, feature extraction, machine learning application, and classifier validation. This investigation differs from previous analyses of Android malware detection by emphasizing additional methods based on machine learning.