On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities

• URL: http://arxiv.org/abs/2306.07118v1
• Date: Mon, 12 Jun 2023 13:52:28 GMT
• Title: On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
• Authors: Masoud Mehrabi Koushki, Ibrahim AbuAlhaol, Anandharaju Durai Raju, Yang Zhou, Ronnie Salvador Giagone and Huang Shengqiang
• Abstract summary: As the smartphone market leader, Android has been a prominent target for malware attacks. For market holders and researchers, in particular, the large number of samples has made manual malware detection unfeasible. While some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time.
• Score: 4.8460847676785175
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all parties involved. 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. However, while some of the proposed approaches achieve high performance, rapidly evolving Android malware has made them unable to maintain their accuracy over time. This has created a need in the community to conduct further research, and build more flexible ML pipelines. 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. 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. In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 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. Drawing from this taxonomy, we also identify gaps in knowledge and provide ideas for improvement and future work.

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