Permission-Based Android Malware Analysis by Using Dimension Reduction With PCA and LDA

Abstract

The expansion of the Android operating system increases the interest of malware developers in this field. With the develop malicious software, the material, and moral harms are given to the users in many ways such as stealing personal data and decreasing device performance. Therefore, the need for systems that detect malware with high accuracy is increasing day by day. In this study, it is aimed to detect malware with a static analysis technique based on machine learning. Because of the limited system resources of mobile devices, principal component analysis and linear discriminant analysis, which are frequently used in machine learning problems with a high number of attributes, are applied for Android malware detection. When the results are examined, it is observed that the dimension reduction techniques have a positive effect on classification performance in general.