Machine Learning for Cyber Security
End-to-end ML pipeline for security threat detection including data preprocessing, feature engineering, model training, and evaluation on real-world security datasets.
Overview
A comprehensive machine learning project applying data science and ML techniques to cybersecurity problems. The project includes building complete ML pipelines with data preprocessing, feature engineering, model development, hyperparameter tuning, and evaluation for security threat detection and classification.
Technologies
Key Features
- ✓Automated data type detection and conversion
- ✓Feature engineering for network traffic data
- ✓Handling imbalanced security datasets
- ✓Multiple ML model comparison
- ✓Cross-validation and hyperparameter tuning
- ✓Security threat classification pipeline
Challenges
Handling highly imbalanced security datasets where attacks are rare, preprocessing noisy network traffic data, developing effective features for threat detection, and building interpretable models for security operations.
Results
Successfully developed machine learning pipelines for cybersecurity applications, demonstrating proficiency in applied ML. Achieved 100% score on the project.