# StellarGuard – Customizable Graph-Powered Threat Intelligence Platform for Stellar Blockchain

StellarGuard 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, StellarGuard 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)

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

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Data Model – Graph-First Design

StellarGuard 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

StellarGuard 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

StellarGuard 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

StellarGuard 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

   git clone https://github.com/yourusername/StellarGuard.git
   cd StellarGuard

2. Copy environment template

   cp .env.example .env

3. Launch services

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
GitHub: @javadTorabiKh

StellarGuard — Turning blockchain data into defense-grade intelligence.

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Built with precision. Powered by graphs. Secured for the future.
#StellarGuard #BlockchainSecurity #GraphAnalytics #Web3