BOTNET Detection Using Machine Learning

The objective of this project is to develop an effective and efficient system for detecting botnet attacks on Android devices using advanced machine learning algorithms. By leveraging the "BotDroid Android Botnet Detection" dataset from Kaggle, the project aims to accurately identify and classify botnet attacks based on network traffic and device behavior data. The system will employ various machine learning techniques, including Random Forest, XGBoost, SVM, and Decision Tree, to achieve high precision and recall in detecting malicious activities. The ultimate goal is to provide a reliable, user-friendly tool that enhances cybersecurity measures for individuals and organizations, protecting Android devices from the significant threats posed by botnet attacks.