FG-Droid: Grouping based feature size reduction for Android malware detection.

ABSTRACT:

Background

The number of applications prepared for use on mobile devices has increased rapidly with the widespread use of the Android OS. This has resulted in the undesired installation of Android application packages (APKs) that violate user privacy or are malicious. The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users.

Methods

In this study, FG-Droid, a machine-learning based classifier, using the method of grouping the features obtained by static analysis, was proposed. It was created because of experiments with machine learning (ML), deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)-

SUBMITTER: Arslan RS 

PROVIDER: S-EPMC9299264 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature