MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis

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Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, background): “DURING the last decade, machine learning has been increasingly applied in security-related tasks, in response to the increasing variability and sophistication of modern attacks [1], [3], [6], [27], [33].”
Adversarial Examples for Malware Detection (2017, methodology): “As an example of an approach combining static and dynamic analysis we mention Marvin[20], which extracts features from an application while running it in an analysis sandbox and observing data flow, network behavior and other operations.”
Transcend: Detecting Concept Drift in Malware Classification Models (2017, methodology): “5Drebin spans the years 2010–2012 while Marvin covers from 2010 to 2014.”
A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “Furthermore, several traditional ML-based Android malware detectors such as TaintDroid [63], Andrubis [64], EnDroid [65], MARVIN [66], and Mobile-sandbox [67] were proposed to utilize the dynamic behavioral features.”