Comparison of Feature Selection Methods in Security Analysis of Android

Abstract

Feature selection as a dimension reduction technique aims to select the subset containing less features by removing unrelated redundant or noisy features. While feature selection generally provides a better recognition performance, it also brings significant gains in calculation cost. In this study, the effects of using the most up-to-date feature selection methods on Android malware detection are shown. In order to observe this effect, test sets in 90 different combinations were prepared and comprehensive experiments were carried out objectively. As a result of the tests, a 4% increase in classification performance was achieved with the recursive feature selection method(RFE), while the gain in calculation cost was 39.39% in the chi2 method. Feature selection in application security analysis in the Android both contributed to the success of classification and reduced the time needed for classification. With this study, it has been shown the feature selection methods are an improvement that can affect the results of studies on Android security.