Abstract Malware detection has become a challenging issue in the field of software engineering. There is an exponential growth of smartphones and apps in the market. Android has gained popularity due to the number of free apps available in its official market. The functionality of apps relies on the permission model. Cybercriminals exploit app permissions to create and distribute malware-infected apps, which they publish in deceptive repositories for smartphone users. This study proposes a malware detection model that works on the principle of feature ranking approaches and a two-stage semi-supervised machine learning classifier YATSI (Yet Another Two-Stage Idea). YATSI is a meta-algorithm that permits distinct machine learning algorithms to apply in the first stage. The experimental result reveals that our proposed algorithm, i.e., YATSIDroid (Artificial Immune System’s Paradigm with YATSI), improves the performance in identifying the malware-infected apps by using the limited labeled dataset as an input. The empirical result shows that YATSIDroid can detect 98.5% of malware-infected apps with 60% of the labeled dataset.
Mahindru et al. (Sat,) studied this question.