文章基本信息

标题：A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection
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作者：Vasileios Kouliaridis ; Georgios Kambourakis
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：5
页码：185
DOI：10.3390/info12050185
出版社：MDPI Publishing
摘要：Year after year, mobile malware attacks grow in both sophistication and diffusion. As the open source Android platform continues to dominate the market, malware writers consider it as their preferred target. Almost strictly, state-of-the-art mobile malware detection solutions in the literature capitalize on machine learning to detect pieces of malware. Nevertheless, our findings clearly indicate that the majority of existing works utilize different metrics and models and employ diverse datasets and classification features stemming from disparate analysis techniques, i.e., static, dynamic, or hybrid. This complicates the cross-comparison of the various proposed detection schemes and may also raise doubts about the derived results. To address this problem, spanning a period of the last seven years, this work attempts to schematize the so far ML-powered malware detection approaches and techniques by organizing them under four axes, namely, the age of the selected dataset, the analysis type used, the employed ML techniques, and the chosen performance metrics. Moreover, based on these axes, we introduce a converging scheme which can guide future Android malware detection techniques and provide a solid baseline to machine learning practices in this field.
关键词：android malware; mobile malware detection; machine learning and classification android malware ; mobile malware detection ; machine learning and classification