Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber
criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud.
These criminals gain numerous benefits as too many people use Android for their daily routines, including important communi-
cations. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used
static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis
requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search (GS), which is a search
based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we
used five machine learning classifiers, namely, Na?ve Bayes (NB), functional trees (FT), J48, random forest (RF), and multilayer
perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use
of only six features.

关键词： Genetic algorithm,  Static analysis,  Android,  Malware,  Machine learning