Android Malware Detection System

Android malware remains a significant threat to mobile security, requiring efficient and scalable detection methods. This project presents an Android Malware Detection System that uses machine learning to classify applications as benign or malicious based on static permission-based analysis. The system is trained on the TUANDROMD dataset of 4,464 applications using four models-Logistic Regression, XGBoost, Random Forest, and Naive Bayes-with a 75/25 train/test split and 5-fold cross-validation on the training set for evaluation. To improve reliability, the system incorporates a hybrid decision approach that combines machine learning confidence scores with a rule-based static analysis engine, using a three-zone confidence routing mechanism to capture threats that ML alone may miss. The solution is deployed as a Flask web application with both a manual detection interface and an APK file scanner, providing predictions, confidence scores, and risk insights, ultimately supporting more informed and secure decision-making.