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