Comprehensive Analysis of Android Malware detection through Semi-supervised Autoencoder models
Neda Firoz, Abdullah bin Firoz, Mohammad Sadman Tahsin · Research Square · 2023
Abstract Recent years have seen a rapid proliferation of malicious software on mobile devices, notably repackaged Android malware. Grasping Android malware detection via vibrant analysis will provide a thorough read, but we must also establish a connection between the app's features and the elements required to fulfill the category's functionality. In this work, we propose a novel method of detecting malwares. This model is a part of semi-supervised learning. We employed autoencoder training features from noisy input. They generate clusters of malicious predictions utilizing the reconstruction error and helped in dimensionality reduction of the large feature set. The newly reconstructed input features are then fed to the machine learning model and Deep learning models for supervised prediction. Later, we contrast their performance based on performance metrics and accuracy. Three datasets were chosen for this experiment to assess the execution and operation of the proposed model. There were two labels in the ‘class’ attribute such as ‘0’ as Benign and ‘1’ as Malicious. The proposed model achieved superior accuracy and can deliver promising results.