Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers

ABSTRACT:

Background

Android is the most widely used operating system all over the world. Due to its open nature, the Android operating system has become the target of malicious coders. Ensuring privacy and security is of great importance to Android users.

Methods

In this study, a hybrid architecture is proposed for the detection of Android malware from the permission information of applications. The proposed architecture combines the feature extraction power of the convolutional neural network (CNN) architecture and the decision making capability of fuzzy logic. Our method extracts features from permission information with a small number of filters and convolutional layers, and also makes the feature size suitable for ANFIS input. In addition, it allows the permission information

SUBMITTER: Atacak I 

PROVIDER: S-EPMC9575934 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature