Detection of Fraud and Malware Apps in Google Play

Abstract

The proliferation of mobile applications on Google Play has led to an increased risk of fraudulent and malicious apps that can compromise user security and privacy. This study presents a comprehensive approach to detecting fraud and malware in Google Play apps using machine learning (ML) and deep learning (DL) techniques. A Flask-based backend is developed to facilitate the upload, processing, and analysis of datasets containing app features and labels. The system utilizes Support Vector Machine (SVM) and Artificial Neural Network (ANN) models to classify apps as benign or malicious. Key stages include data preprocessing, dataset splitting, model training, and performance evaluation. The models' accuracies are compared, and results are visualized to demonstrate their effectiveness. Additionally, an APK upload feature simulates real-time analysis and prediction, enhancing the practical applicability of the solution. The integration of ML and DL methods provides a robust framework for proactively identifying and mitigating threats posed by fraudulent and malware apps in the Google Play ecosystem.