This PR delivers a comprehensive README overhaul to reflect the Phase 7 Machine Learning Integration. It transforms the documentation from a pre-ML state to a world-class README that accurately represents AltFlex as a full-stack exploit intelligence platform with ML-powered pattern recognition.
- Badge Row: Added
XGBoost,scikit-learn,ONNX Runtime, andPythonbadges. - Overview Section: Added the "🧠 ML Exploit Pattern Recognizer" module and expanded the Module Capability Matrix to 3 columns.
- Key Features: Added "🧠 Machine Learning Intelligence" block with 6 feature highlights.
- Architecture Diagram: Updated the Mermaid graph with a new ML Intelligence subgraph (
OnnxExploitClassifier,TraceFeatureExtractor), ONNX model node, and connection arrows. - Tech Stack: Added 6 ML-specific rows (XGBoost, scikit-learn, ONNX Runtime, pandas/numpy, matplotlib/seaborn, Python).
- Monorepo Structure: Expanded the tree to show
adapters/ml/, the fullresearch/sub-structure (datasets, models, figures, reports), andscripts/ml/. - ML Model Performance (NEW section): Embedded the Heuristic vs. XGBoost comparison table, per-pattern Δ metrics, and all 6 thesis figures in a 2×3 grid.
- Phase Roadmap: Added Phase 7 row and full Phase 7 Task Tracker (7 tasks with PR links).
- Academic Alignment: Added Phase 7 → Thesis 1 & 2 mapping.
- Package Dependency Graph: Updated
@aegis/forensic-engineto showonnxruntime-nodedependency andscripts/ml/→ ONNX model pipeline. - Changelogs: Added
[03.7.0]entry documenting all ML sub-tasks (P7-ML-001 through P7-ML-007). - Table of Contents: Added "ML Model Performance" entry.
- Documentation update (non-breaking, no code changes)
- My changes follow the project's documentation style
- All internal links resolve correctly
- Mermaid diagrams render without errors
- Embedded figures reference existing files in
research/figures/