Malware Classifier: ML-Powered PE File Detection | Quantic MSSE Intro to machine learning Project скачать в хорошем качестве

Malware Classifier: ML-Powered PE File Detection | Quantic MSSE Intro to machine learning Project

Malware Classifier using Machine Learning Quantic School of Business and Technology - MSSE Program This video demonstrates a production-ready malware classification system that analyzes PE (Portable Executable) files using machine learning. The project was completed in partial fulfillment of the Introduction to Machine Learning course at Quantic School of Business and Technology. Live Application: https://tetteh-apotey-malware-classif... GitHub Repository: https://github.com/life2allsofts/malw... (Private - quantic-grader added as collaborator) PROJECT OVERVIEW The application uses an XGBoost model with 17 PE header features to classify executable files as malware or benign software. Key features include: 98.03% accuracy on test set 99.59% AUC-ROC for excellent discrimination 17 PE header features (no data leakage) Bias correction with 0.6 threshold Fully automated CI/CD pipeline (51 successful runs) APPLICATION FEATURES File Upload Analysis Upload .exe, .dll, .sys, .ocx, .scr, .cpl files Extracts SHA-256 hash and entropy Real-time prediction with confidence scores Manual Input Enter all 17 PE features manually Sample templates for testing Understand how features influence predictions Batch Processing CSV upload for multiple files Download predictions.csv with results Ideal for bulk analysis Model Information Feature importance visualization Confusion matrix and performance metrics Complete transparency CI/CD PIPELINE The project includes a fully automated GitHub Actions pipeline that: Runs tests on every push (16 tests in 46 seconds) Checks for prior bias and model sanity Auto-deploys to Hugging Face Spaces on success Performs smoke tests to verify deployment Total workflow runs: 51 | Latest status: Passing MODEL PERFORMANCE Metric Value Accuracy 98.03% Precision 98.24% Recall 98.37% F1-Score 98.30% AUC-ROC 99.59% Confusion Matrix (Test Set): Predicted BENIGN MALWARE Actual BENIGN 1648 42 Actual MALWARE 38 2287 False Positives: 42 False Negatives: 38 Total Errors: 80 (1.99% error rate) TECHNOLOGIES USED Machine Learning: XGBoost, scikit-learn, pandas, numpy Web Framework: Flask, Jinja2 templates Deployment: Hugging Face Spaces, Docker CI/CD: GitHub Actions AI Tools: DeepSeek AI (97%), ChatGPT (2%), GitHub Copilot (1%) DOCUMENTATION All project documentation is available in the GitHub repository: Evaluation and Design: https://github.com/life2allsofts/malw... AI Tooling Strategy: https://github.com/life2allsofts/malw... Deployment Information: https://github.com/life2allsofts/malw... Results and Metrics: https://github.com/life2allsofts/malw... ABOUT THE DEVELOPER Isaac Tetteh-Apotey MSSE Candidate, Quantic School of Business and Technology Geomatics Engineer & Software Engineering Researcher GitHub: https://github.com/life2allsofts Portfolio: https://tetteh-apotey.vercel.app/ LinkedIn:   / isaac-tetteh-apotey-67408b89   PROJECT TIMELINE Started: February 17, 2026 Completed: February 28, 2026 Development Time: 11 days CI/CD Runs: 51 successful workflows DISCLAIMER This application is intended for educational and research purposes only. The model should not be used as the sole determinant for malware classification in production environments without additional validation. For questions about this project, please reach out via GitHub or LinkedIn.