fix reademe.md

Author: JavadTorabiKh

README.md

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+# StellarChainAnalysis – Customizable Graph-Powered Threat Intelligence Platform for Stellar Blockchain
+
+![Stellar Logo](./static/Stellar-1.png)
+
+**StellarChainAnalysis** is a **highly customizable, modular, and user-configurable** blockchain analytics and threat intelligence platform for the **Stellar Network**. It ingests raw on-chain data, builds a **dynamic, queryable graph model**, and empowers users to **interact with, extend, and personalize the resulting graph** based on their specific use case — whether security, compliance, research, or business intelligence.
+
+Unlike rigid tools, StellarChainAnalysis is designed as a **flexible infrastructure**:  
+- Users can **define custom node types, relationships, and properties**  
+- **Create personalized risk models** using configurable rules and ML pipelines  
+- **Interact directly with the graph** via APIs, visual queries, or embedded dashboards  
+- **Export, extend, or integrate** the graph into external systems (BI tools, SIEM, notebooks)
+
+Built for **security teams, auditors, researchers, and enterprises**, it supports **mass customization** — enabling each deployment to evolve into a **tailored on-chain intelligence engine**.
+
+
+## Project Vision
+
+To create a **trusted, scalable, and extensible security operations platform** that empowers the Stellar ecosystem with proactive threat intelligence, forensic capabilities, and transparent risk assessment.
+
+## Core Objectives
+
+- **Enable users to define and detect** any custom transaction pattern in real time using **configurable rules, Cypher queries, or ML models**  
+- **Allow full control over graph enrichment** — users can **add custom node types, properties, relationships, and enrichment pipelines** to model domain-specific logic  
+- **Support dynamic, user-defined risk scoring** — combine built-in heuristics with **custom formulas, external signals, or trained models**  
+- **Provide deep, interactive graph exploration** — users can **query, filter, traverse, and manipulate the live graph** through visual tools or direct API access  
+- **Deliver fully configurable alerting** — route alerts based on **user-defined thresholds, channels, and enrichment context** (Slack, email, SIEM, webhooks)  
+- **Empower advanced forensic workflows** — with **custom graph snapshots, time-travel queries, export formats, and integration hooks** for Jupyter, BI tools, or internal platforms
+
+
+## Architecture (C4 Model – Container Diagram)
+
+```mermaid
+graph LR
+    %% External Services
+    subgraph External["External Services"]
+        H["Stellar Horizon API\n(horizon.stellar.org)"]
+    end
+
+    %% Ingestion Layer
+    subgraph Ingestion["Ingestion Layer"]
+        I["Horizon Stream Listener\n(WebSocket / SSE)"]
+    end
+
+    %% Messaging
+    subgraph Messaging["Event Bus"]
+        K["Redis Streams\n(Pluggable: Kafka, RabbitMQ)"]
+    end
+
+    %% Processing Layer
+    subgraph Processing["Processing Layer (User-Extensible)"]
+        P["Transaction Processor\n(Custom Parser Plugins)"]
+        G["Graph Enricher\n(Custom Cypher + APOC)"]
+        M["ML Anomaly Detector\n(User-Trainable Models)"]
+    end
+
+    %% Storage
+    subgraph Storage["Graph Storage"]
+        N[(Neo4j Graph DB\nAPOC + GDS + Custom Indexes)]
+    end
+
+    %% API & Interaction
+    subgraph API["User Interaction Layer"]
+        A["REST + GraphQL API\n(Custom Endpoints & Queries)"]
+        Q["Interactive Query Console\n(Live Cypher + Visual Builder)"]
+    end
+
+    %% Frontend
+    subgraph Frontend["Customization Dashboard"]
+        D["Configurable Dashboard\n(Drag-and-Drop + Widgets)"]
+        C["Graph Studio\n(Cytoscape.js + Custom Layouts)"]
+    end
+
+    %% Alerting
+    subgraph Alerting["Alerting & Automation"]
+        S["Configurable Alert Engine\n(Rules + Webhooks + SIEM)"]
+    end
+
+    %% Data Flow
+    H --> I
+    I --> K
+    K --> P
+    K --> G
+    K --> M
+    P --> M
+    G --> N
+    M --> N
+    N --> A
+    N --> Q
+    A --> D
+    Q --> C
+    M --> S
+    N --> S
+```
+
+## Data Model – Graph-First Design
+
+StellarChainAnalysis treats the blockchain as a **living, user-extensible graph** — not a fixed schema. While it starts with a minimal core model for Stellar transactions, **every aspect of the graph is designed for mass customization and direct user interaction**.
+
+### Core Model (Starting Point)
+- **Accounts** – Public keys with balance and activity metadata  
+- **Transactions** – Ledger operations with fee, sequence, and result  
+- **Assets** – XLM or custom tokens involved in payments  
+
+### User-Defined Extensions
+Users can **freely extend** the graph at runtime:
+- Add **custom node types** (e.g., `SuspiciousCluster`, `ComplianceTag`, `UserProfile`)  
+- Define **new relationship types** (e.g., `FLAGGED_BY`, `LINKED_VIA_MEMO`, `PART_OF_CAMPAIGN`)  
+- Attach **arbitrary properties** (e.g., `kyc_status`, `risk_source`, `user_notes`)  
+- Create **virtual nodes/edges** via Cypher projections for analysis  
+
+### Live Graph Interaction
+Through the **Graph Studio** and **Query Console**, users can:
+- **Traverse** the graph with custom Cypher queries  
+- **Merge, split, or relabel** nodes in real time  
+- **Create persistent views** (e.g., "All accounts linked to address X in last 7 days")  
+- **Export subgraphs** in JSON, CSV, or GEXF for external tools  
+
+> The graph is **not just storage** — it's a **collaborative workspace** where analysts, compliance officers, and data scientists build and refine intelligence together.
+
+## Threat Detection Capabilities
+
+StellarChainAnalysis identifies a wide range of malicious or suspicious behaviors:
+
+| Threat Type | Detection Method |
+|-----------|------------------|
+| **Money Laundering** | Cycle detection in payment paths |
+| **Pump & Dump** | Sudden volume surges + rapid sell-off |
+| **Wash Trading** | Self-referential transaction loops |
+| **Account Takeover** | Anomalous login or signing patterns |
+| **Phishing Drains** | Outbound flows to known malicious sinks |
+| **Memo Abuse** | Encoded commands or C2 communication |
+
+Each detection contributes to a **dynamic risk score** (0–100) updated in real time.
+
+## Real-Time Dashboard
+
+The web-based dashboard delivers an **intuitive, analyst-friendly interface** with:
+
+- **Live Activity Feed** – Chronological view of incoming transactions  
+- **Risk Leaderboard** – Top 50 highest-risk accounts with trend indicators  
+- **Interactive Network Graph** – Explore transaction flows with zoom, search, and filtering  
+- **Heatmap View** – Geographic or cluster-based risk density  
+- **Alert Timeline** – Visual log of triggered notifications  
+
+All visualizations update instantly via **WebSocket push technology**.
+
+## Machine Learning Integration
+
+StellarChainAnalysis combines **unsupervised and graph-aware ML models**:
+
+### Anomaly Detection
+- **Isolation Forest** on transaction velocity, volume, and counterparty diversity  
+- **Autoencoders** for learning normal behavioral embeddings  
+
+### Graph Neural Networks
+- Trained on historical subgraphs to predict node-level risk  
+- Leverages structural patterns invisible to traditional models  
+
+Models are retrained periodically using labeled datasets and feedback loops.
+
+## API & Integration
+
+The platform exposes a **versioned REST API** for integration with external tools:
+
+### Example Endpoints
+- `GET /api/v1/accounts/{address}` → Full profile, risk score, and recent activity  
+- `GET /api/v1/transactions/{hash}` → Transaction details + local graph context  
+- `GET /api/v1/alerts?since=24h` → Recent high-risk events  
+- `GET /api/v1/export/graph?center={address}&depth=2` → Export ego-network in JSON  
+
+All responses follow **OpenAPI 3.0** specification.
+
+## Deployment & Operations
+
+StellarChainAnalysis is fully **containerized** and designed for both development and production:
+
+### Local Development
+- Uses **Docker Compose** to spin up Neo4j, Redis, and the application  
+- Pre-configured datasets for rapid prototyping  
+
+### Production Readiness
+- Structured logging (JSON format)  
+- Health checks and metrics endpoints  
+- Rate limiting and API key authentication  
+- Secure configuration via environment variables  
+- CI/CD pipeline with automated testing  
+
+Future versions will support **Kubernetes** and **cloud-native observability**.
+
+
+## Getting Started
+
+### Prerequisites
+- Docker & Docker Compose
+- Python 3.11+
+- Internet access to `horizon.stellar.org`
+
+### Quick Start
+
+1. **Clone the repository**
+   ```bash
+   git clone https://github.com/yourusername/StellarChainAnalysis.git
+   cd StellarChainAnalysis
+    ```
+2. Copy environment template
+   ```bash
+   cp .env.example .env
+   ```
+   
+3. Launch services
+ ```bash
+docker-compose up -d
+```
+
+4. Access the platform
+    - Dashboard: http://localhost:5000
+    - Neo4j Browser: http://localhost:7474
+    - API Docs: http://localhost:5000/api/docs
+
+## Testing & Quality
+
+The project includes a comprehensive test suite:
+- **Unit tests** for individual components  
+- **Integration tests** with live Neo4j containers  
+- **End-to-end scenarios** simulating real attack patterns  
+
+All code adheres to **PEP 8** and is validated via **Flake8** and **Black**.
+
+## Security Considerations
+
+- API keys required for all external access  
+- Input validation on all public endpoints  
+- Secrets never committed to version control  
+- Regular dependency scanning (Safety, Bandit)  
+- Principle of least privilege in database permissions
+
+## Roadmap
+
+| Version | Milestone |
+|--------|----------|
+| **v1.0** | Real-time monitoring + basic ML + dashboard |
+| **v1.5** | Historical backfill + export tools |
+| **v2.0** | FastAPI migration + GraphQL support |
+| **v2.5** | Multi-chain connectors (Solana, Ethereum) |
+| **v3.0** | Public API + community threat sharing |
+
+## Contributing
+
+We welcome contributions! To get involved:
+
+1. Fork the repository  
+2. Create a feature branch (`feature/your-idea`)  
+3. Write clean, tested code  
+4. Submit a pull request with clear description  
+
+Please follow our **Code of Conduct** and **Contribution Guidelines**.
+
+## Academic & Research Use
+
+This project was originally developed as part of a master's thesis on **Graph-Based Anomaly Detection in Payment Networks**. Supplementary materials:
+- Full architecture diagrams  
+- Performance benchmarks  
+- Model evaluation results  
+- Cypher query library  
+
+Available in the `/docs` directory.
+
+## License
+MIT License
+Copyright (c) 2025 javad torabi
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction...
+
+
+## Contact
+
+**Project Maintainer**  
+Email: [j.2528840@gmail.com](mailto:j.2528840@gmail.com)  
+GitHub: [@javadTorabiKh](https://github.com/javadTorabiKh)  
+
+
+> **StellarChainAnalysis** — *Turning blockchain data into defense-grade intelligence.*
+
+---
+
+*Built with precision. Powered by graphs. Secured for the future.*  
+**#StellarChainAnalysis #BlockchainSecurity #GraphAnalytics #Web3**
+