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As in other cybersecurity areas, machine learning (ML) techniques haveemerged as a promising solution to detect Android malware. In this sense, manyproposals employing a variety of algorithms and feature sets have beenpresented to date, often reporting impresive detection performances. However,the lack of reproducibility and the absence of a standard evaluation frameworkmake these proposals difficult to compare. In this paper, we perform ananalysis of 10 influential research works on Android malware detection using acommon evaluation framework. We have identified five factors that, if not takeninto account when creating datasets and designing detectors, significantlyaffect the trained ML models and their performances. In particular, we analyzethe effect of (1) the presence of duplicated samples, (2) label(goodware/greyware/malware) attribution, (3) class imbalance, (4) the presenceof apps that use evasion techniques and, (5) the evolution of apps. Based onthis extensive experimentation, we conclude that the studied ML-based detectorshave been evaluated optimistically, which justifies the good published results.Our findings also highlight that it is imperative to generate realisticexperimental scenarios, taking into account the aforementioned factors, tofoster the rise of better ML-based Android malware detection solutions.

Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning