Journal
COMPUTERS & SECURITY
Volume 124, Issue -, Pages -Publisher
ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.cose.2022.102996
Keywords
Android malware detection; Machine learning; Mobile security; Experimental analysis; Static analysis
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In this paper, an analysis is conducted on ten influential research works on Android malware detection, identifying five factors that significantly affect the trained ML models and their performances. It is emphasized that generating realistic experimental scenarios and considering these factors is crucial for the development of better ML-based Android malware detection solutions.
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. (c) 2022 Elsevier Ltd. All rights reserved.
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