![]() And, in turn, has made it essential for the defenders (e.g., cybersecurity researchers, mobile app store holders, or antivirus companies) to try and automate the detection process. This large number of samples has rendered manual analysis and classification of them infeasible. A similar trend was observed by McAfee from 2018 to 2019 when the total number of mobile malware samples increased by over 25% (McAfee 2021). Kaspersky, for example, reported detecting over 5.5 million malicious packages in the year 2020, which was a 62% increase compared to 2019 (Kaspersky 2021). Reports show that the number of new malware samples discovered for Android has been increasing steadily over the past decade. Drawing from this taxonomy, we also identify gaps in knowledge and provide ideas for improvement and future work.Īndroid has become the primary target of mobile malware attacks, due, in no small part, to its high market share (StatCounter 2021). We introduce a novel procedural taxonomy of the published literature, covering how they have used ML algorithms, what features they have engineered, which dimensionality reduction techniques they have employed, what datasets they have employed for training, and what their evaluation and explanation strategies are. In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021). Existing survey papers often focus only on parts of the ML process (e.g., data collection or model deployment), while omitting other important stages, such as model evaluation and explanation. ![]() Doing so, however, is currently hindered by a lack of systematic overview of the existing literature, to learn from and improve upon the existing solutions. This has created a need in the community to conduct further research, and build more flexible ML pipelines. ![]() However, while some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time. For market holders and researchers, in particular, the large number of samples has made manual malware detection unfeasible, leading to an influx of research that investigate Machine Learning (ML) approaches to automate this process. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. ![]() As the smartphone market leader, Android has been a prominent target for malware attacks. ![]()
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