A production-grade machine learning system designed to classify and analyze cybersecurity incidents using Microsoft's GUIDE dataset. Combines state-of-the-art ML techniques with cybersecurity domain expertise.

• ✅ Complete Data Pipeline: Advanced preprocessing and feature engineering
• ✅ Multiple ML Models: XGBoost, Random Forest, MLP Neural Networks
• ✅ MITRE ATT&CK Integration: Threat technique mapping and analysis
• ✅ Production-Ready: Inference system (currently in development)
• ✅ Scalable Architecture: Designed for enterprise deployment

┌─────────────────────────────┐
│  Core Technologies          │
├─────────────────────────────┤
│  • Python                   │
│  • XGBoost                  │
│  • PyTorch                  │
│  • scikit-learn             │
│  • pandas                   │
├─────────────────────────────┤
│  Domain Frameworks          │
├─────────────────────────────┤
│  • MITRE ATT&CK             │
│  • Security Analytics       │
└─────────────────────────────┘

Built by engineers who understand both data science and cybersecurity, creating a solution that's technically sophisticated and practically effective.

Deep Learning & Machine Learning

• PyTorch for neural network development
• TensorFlow for production ML systems
• scikit-learn for classical ML algorithms
• XGBoost for gradient boosting excellence

Data Manipulation & Visualization

• pandas for data manipulation
• NumPy for numerical computing
• matplotlib & seaborn for visualization
• Jupyter for interactive analysis

Development & Deployment

• Git for version control
• Docker for containerization
• CI/CD for automated pipelines
• Linux for production environments

Security & Threat Intelligence

• MITRE ATT&CK framework
• Security analytics platforms
• Threat detection systems
• Incident response tools

Exploring cutting-edge technologies in AI and data science

Focus Areas:

• Novel ML algorithms
• Advanced deep learning
• Security AI systems
• Emerging technologies

Building end-to-end solutions from concept to deployment

Deliverables:

• Production ML systems
• Data pipelines
• Security platforms
• Automated workflows

Tackling real-world challenges with data-driven approaches

Methodology:

• Root cause analysis
• Predictive modeling
• Pattern recognition
• Optimization strategies

Documenting our work and contributing to the community

Contributions:

• Open-source projects
• Technical documentation
• Best practices
• Community engagement