Towards a Fair Comparison and Realistic Design and Evaluation Framework of Android Malware Detectors. (arXiv:2205.12569v1 [cs.CR])

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 …

android android malware design fair framework malware