Abstract: The growing number of malicious applications on the Android platform has raised significant security concerns, necessitating the development of effective detection mechanisms. This project focuses on categorizing Android applications based on their potential malicious behavior using machine learning techniques, specifically Artificial Neural Networks (ANN) and Support Vector Machines (SVM). A dataset consisting of 410 unique permission combinations, sourced from Kaggle, serves as the foundation for identifying key features that distinguish between benign and harmful applications.
The project employs two classification models: ANN, which leverages deep learning to analyze complex permission patterns, and SVM, a traditional yet highly effective machine learning algorithm known for its precise decision boundaries. Additionally, the system includes a real-time malware detection web application built using Flask. Users can upload APK files, after which the system extracts permission-based features and applies the trained models to determine whether the application is benign or malicious. By integrating ANN and SVM, this project highlights the effectiveness of permission-based machine learning models in Android malware detection. The proposed approach strengthens mobile cybersecurity by demonstrating how advanced machine learning techniques can be utilized to combat modern security threats.
Keywords: Android security, malware detection, machine learning, Artificial Neural Networks (ANN), Support Vector Machines (SVM), APK permissions, deep learning, cybersecurity, Flask web application, mobile threat analysis.
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DOI:
10.17148/IJARCCE.2025.14543
