Feature Extraction and Classification in Android Malware Detection with the Integration of Autoencoders and Transfer Learning

Abstract

Introduction: Mobile security suffers greatly by the quick spread of Android malware, leading to the need for sophisticated detection methods that can change to meet new threats. Malware is still a difficult security issue in the Android ecosystem because it frequently obfuscates itself to avoid detection. Semantic behavior feature extraction is essential in this situation in order to build a reliable malware detection model

Objectives: To provide an overview of  android malware, including the impact of malware detection, the significance of malware detection, its types, and a framework for detecting Android malware that uses Transfer Learning (TL) to forecast malware in the Android ecosystem and AEs (AutoEncoders) to extract features.

Methods: This study presents an integrated deep learning (DL) method for Android malware detection that combines AEs for feature extraction and TL for classification. In order to effectively depict both benign and malevolent actions, AEs are used to extract latent, highly dimensional features from both static and dynamic analytical data. Then, TL  uses deep neural networks that have already been trained to identify Android apps more accurately and with less training time

Results: The tests were conducted on three datasets with two labels in the "class" attribute "0" for benign and "1" for malicious in order to evaluate the effectiveness of the suggested framework. With an enhanced MAE value of 0.001 and RMSE value of 0.063 attain  an  99.99% accuracy. The findings show that the proposed model achieved remarkable accuracy and has the potential to produce reliable malware detection results

Conclusions: An integrated DL strategy for Android malware detection that combines AEs for feature extraction and TL for classification has been presented. AEs are used to extract high-dimensional, latent characteristics from both static (code-related) and dynamic (behavior-related) Android app analysis data. The system may thus effectively capture and reflect both beneficial and harmful behaviors.