Welcome to the Malware Detection and Vulnerability Analysis Project! This project aims to develop a comprehensive system for identifying potential vulnerabilities and malware threats across various platforms, including websites, APK files, and network configurations. Leveraging data from trusted sources like CVE, Exploit-DB, VirusTotal, and more, this project integrates machine learning to provide actionable insights and enhance cybersecurity defenses.
Features Data Integration: Aggregates data from multiple sources, including CVE, Exploit-DB, VirusTotal, and network analysis tools. Feature Engineering: Transforms raw data into meaningful features for machine learning models. Machine Learning Model: Utilizes a Random Forest classifier to predict vulnerabilities based on historical data and current threats. Modular Design: Organized into separate modules for data fetching, feature engineering, model training, and prediction. Logging: Comprehensive logging system for tracking data processing and model performance. Directory Structure config/: Contains configuration files for API endpoints and model parameters. data/: JSON files with sample data for CVE, exploits, VirusTotal, network analysis, and APK analysis. src/: Source code for data fetching, feature engineering, model training, and prediction. data_fetcher.py: Functions for retrieving and loading data from various sources. feature_engineering.py: Creates feature sets for machine learning from raw data. model_trainer.py: Trains a machine learning model on the feature set. predictor.py: Makes predictions using the trained model. logger.py: Configures and handles logging for the project. main.py: The entry point of the project, which orchestrates data fetching, feature engineering, model training, and prediction. Installation and Usage Clone the Repository:
bash Copy code git clone https://github.com/yourusername/malware_detection_project.git cd malware_detection_project Set Up the Environment: Install the required Python packages:
bash Copy code pip install -r requirements.txt Run the Script: Execute the main script to start the project:
bash Copy code python main.py This will process the data, train the model, and make predictions.
Configuration Modify the config/api_config.json and config/model_config.json files to set your API keys and model parameters.
Contributing Contributions are welcome! Please submit issues and pull requests to help improve the project.
License This project is licensed under the MIT License - see the LICENSE file for details.
Contact For questions or further information, please contact vamshi112211krishna@gmail.com.