Project Name
CKB Wallet Behaviour Intelligence
Team
- GitHub: FadhilMulinya
- Role: Product engineer with experience building blockchain infrastructure, indexing systems, backend services, and developer tooling
- Telegram: mulinyaaa
- Email: [email protected]
Project Description
Problem
CKB provides transparent and rich on-chain data through its Cell-based architecture, but there is currently no open-source tooling that converts raw transaction data into actionable behavioral intelligence.
Developers can easily access transaction histories through explorers and APIs, but understanding how a wallet behaves still requires significant custom analysis and infrastructure.
Questions such as:
- Is this wallet behaving like an individual user?
- Does this wallet exhibit highly automated activity?
- Is this wallet likely operating as an exchange-controlled wallet?
- Does this wallet demonstrate unusual transaction behavior compared to typical network participants?
cannot currently be answered through a simple API or reusable ecosystem tool.
As the CKB ecosystem grows, analytics platforms, wallet providers, security tooling, explorers, and future infrastructure services can benefit from richer wallet context rather than relying solely on raw transaction records.
This project is not focused on detecting low-cost Sybil attacks. CKB’s Cell model and state occupation costs already increase the cost of mass wallet creation. Instead, the project focuses on behavioral analysis and wallet classification, providing ecosystem developers with reusable analytics infrastructure built specifically for CKB.
Solution
We propose a lightweight Wallet Analytics & Classification API for the CKB ecosystem.
The system will analyze wallet transaction histories using machine learning and behavioral feature extraction.
The initial version will focus on:
- Transaction timing patterns
- Transaction frequency patterns
- Wallet interaction graph relationships
- Cell usage and transaction flow characteristics
Using these features, the system will generate:
- Human-like behavior score
- Automated behavior score
- Exchange-like behavior score
- Confidence metrics
Developers will be able to submit a CKB wallet address through an API or web dashboard and receive a behavioral classification result.
The goal of this Spark Program project is to deliver a working prototype that demonstrates how behavioral analytics can enrich wallet intelligence across the CKB ecosystem.
Expected Deliverables
1. Open Source Repository
A public GitHub repository containing:
- CKB data extraction pipeline
- Feature engineering pipeline
- Model training code
- API implementation
- Deployment instructions
- Technical documentation
2. Wallet Classification API
A public REST API capable of:
- Accepting a CKB wallet address
- Returning wallet classification results
- Returning confidence scores
3. Web Dashboard
A simple dashboard allowing users to:
- Input wallet addresses
- View classification results
- View behavioral indicators and scores
4. Initial Wallet Dataset
A curated dataset containing approximately:
- 500 human-like wallets
- 500 automated and / or exchange-like wallets
5. Documentation
Documentation covering:
- API usage
- Deployment instructions
- Model methodology
- Evaluation results
6. Public Demonstration
- Live demo deployment
- Demonstration video
- Project completion report
How To Verify
The project can be verified without requiring code review.
API Verification
Users can submit a CKB wallet address to the public API and receive:
- Behavioral classification
- Confidence scores
Dashboard Verification
Users can access the public dashboard and classify wallet addresses directly through the interface.
Repository Verification
Community members can:
- Access the public GitHub repository
- Review documentation
- Reproduce model training and deployment
Demo Verification
Reviewers can:
- Access the deployed demonstration
- Test known wallet addresses
- Verify expected outputs
Documentation Verification
Users can follow the provided setup guide and deploy the system locally.
Example integration: A Randomn wallet Provider
**
Required Funding
Developer Time
| Phase | Hours | Description |
|---|---|---|
| Data extraction and feature engineering | 30 hrs | CKB-CCC integration, Explorer API integration, transaction ingestion pipeline, feature engineering |
| Model development and evaluation | 30 hrs | Dataset preparation, model training, model evaluation, classification pipeline |
| API and dashboard implementation | 25 hrs | REST API development, dashboard implementation, model serving integration |
| Testing, deployment and documentation | 25 hrs | QA testing, deployment, documentation, demo preparation and final reporting |
| Total Developer Time | 110 hrs | Approximately 6 weeks of development |
Infrastructure & Deployment Costs
| Item | Unit Cost | Quantity | Subtotal (USD) |
|---|---|---|---|
| VPS hosting (API, dashboard, model inference server) | $12/month | 2 months | $24 |
| Database | $20/month | 2 months | $40 |
| Domain name for public API and demo | ~$12/year | 1 | $12 |
| Infrastructure & Deployment Subtotal | $76 |
These costs support the public deployment of the Wallet Analytics & Classification API, dashboard hosting, model serving, dataset storage, monitoring, testing, and demonstration infrastructure required for the Spark Program deliverables.
Developer Compensation
| Item | Amount |
|---|---|
| Total grant requested | $1,000 |
| Infrastructure & deployment costs | $76 |
| Dataset preparation & validation | $124 |
| Remaining allocated to developer time | $800 |
Effective Hourly Rate
| Metric | Value |
|---|---|
| Total developer hours | 110 hrs |
| Developer budget | $800 |
| Effective hourly rate | $7.27/hr |
The requested funding supports approximately 110 hours of development work covering blockchain integration, wallet data extraction, feature engineering, machine learning implementation, API development, dashboard development, testing, deployment, documentation, and public demonstration delivery.
The effective rate of approximately $7.27/hour reflects the prototype-focused nature of the Spark Program and the goal of delivering a functional open-source proof of concept for the CKB ecosystem within a limited funding budget.
Estimated Completion Time
6 weeks total
- 4 weeks active development
- 2 weeks testing, documentation, and deployment
The project scope is intentionally limited to delivering a verifiable prototype suitable for ecosystem validation and future iteration.
Clear To-Do List
Week 1
- Integrate CKB-CCC
- Integrate Explorer API
- Build transaction ingestion pipeline
Week 2
- Implement feature extraction pipeline
- Create wallet labeling workflow
- Build initial dataset
Week 3
- Complete labeled dataset
- Train classification model
- Evaluate model performance
Week 4
- Develop REST API
- Develop web dashboard
- Integrate model inference service
Week 5
- Conduct testing
- Improve classification quality
- Collect initial feedback
Week 6
- Deploy public demo
- Publish documentation
- Release open-source repository
- Publish completion report
Relevance to the CKB Ecosystem
Meeting Actual Needs in the CKB Ecosystem
The project provides a reusable wallet analytics layer that can benefit:
- Wallet providers
- Blockchain explorers
- Analytics platforms
- Security monitoring tools
- Future infrastructure services
Today, teams that require wallet behavior analysis must build their own infrastructure and classification logic from scratch.
This project provides an open-source foundation that can be reused and extended by ecosystem participants, reducing duplicated effort while encouraging experimentation with behavioral analytics on CKB.
The project also contributes an initial labeled wallet dataset and wallet classification API that can serve as a starting point for future ecosystem tooling.
Utilizing CKB’s Technical Architecture
The project leverages CKB’s transparent transaction model and Cell-based architecture to extract behavioral signals that are difficult to obtain from simple account balances alone.
Using transaction flows, Cell consumption patterns, transaction timing, and wallet interaction relationships, the system demonstrates how machine learning can be applied to CKB-specific data structures to generate useful wallet insights.
This showcases a practical analytics use case built directly on top of CKB’s unique architecture and provides a foundation for future wallet intelligence and ecosystem analytics tools.