Android Malware Detection System

Abstract

ABSTRACT Python-based web application developed to identify and analyse malicious Android APK files. It integrates static analysis, using Andro guard to extract permissions and metadata, with dynamic analysis, employing Mobs to monitor runtime behaviour in a controlled sandbox environment. This dual-layered approach enables the detection of both overt and hidden threats. At its core, the system leverages a Random Forest classifier trained on labelled datasets of malware and benign applications, providing accurate threat classification along with confidence scoring. The application features a Flask-based web interface that allows secure APK uploads, real-time scanning, and the generation of detailed PDF reports. A history of previous scans is maintained to support long-term tracking and analysis. The technical architecture consists of three main components: a user interface built with HTML, CSS, and Flask templates; a Python backend responsible for feature extraction, safe testing, and machine learning predictions; and an SQLite database for storing results. By automating the identification of dangerous applications, the system addresses critical mobile security challenges. It is designed with scalability in mind, making it easy to expand for future needs such as cloud integration. Built with Python’s security tools and thoroughly tested, the project demonstrates a practical solution to combat evolving Android malware. It ensures usability for both programmers and security experts, combining technical rigor with accessibility. Ultimately, the Android Malware Detection System provides a comprehensive, reliable, and user-friendly platform to safeguard mobile ecosystems against emerging threats.