Malware Detection Using Static Feature Analysis and Deep Learning Techniques

A Static Robust DL-Based Model for Android Malware Detection

Abstract

The increasing use of Android mobile devices and applications leads to an increase in malware threats. There is a requirement to investigate if a more detailed feature extraction from APK files with deep learning can produce more accurate results. We investigate using deep learning techniques to detect Android Malware considering the latest datasets. We aim to improve the system’s ability to accurately classify and detect a wider range of Android malware variants. We propose a mechanism to carry out APK analysis for feature extraction capable of extracting 46,648 features. We retain 10,523 features after applying feature selection and subsequently use these selected features to train the neural networks. We make use of APK retrieved from Androzoo for dataset generation. We contribute a dataset with code and scripts to arrive at our proposed dataset using a public repository. We compare deep learning models based on deep neural networks (DNN), convolutional neural networks (CNN), and transfer learning-based models using static features. We consider our contributed datasets and conclude that the DNN-based models outperform the CNN models with a wider range and number of features.