CyBlocks: Blockchain Fraud Detection
Advanced blockchain analytics platform for real-time fraud detection and transaction monitoring
Project Overview
Created an advanced blockchain analytics platform for Verity Ignite, providing real-time fraud detection and transaction monitoring with unprecedented accuracy.
Challenges
- Traditional fraud detection systems were ineffective for blockchain transactions
- High volume of transactions required real-time processing capabilities
- Complex fraud patterns across multiple blockchain networks were difficult to detect
- Needed to balance false positives with comprehensive detection
Our Approach
- Developed specialized machine learning models for blockchain transaction analysis
- Created a scalable architecture capable of processing millions of transactions per hour
- Implemented graph analysis to detect complex relationships between transactions
- Built a real-time alerting system with configurable risk thresholds
Technologies Used
Key Results
- Detected fraudulent transactions with 96% accuracy
- Reduced investigation time by 83%
- Saved clients an estimated $12M in potential losses
- Provided unprecedented visibility into blockchain transactions
- Supported monitoring across 6 major blockchain networks
- Processed over 125 million transactions monthly by year end
Project Timeline
Project Timeline
The CyBlocks platform was developed over a 12-month period, following a structured approach that enabled Verity Ignite to build a comprehensive blockchain fraud detection system. Click on each phase to learn more about the activities and deliverables.
Research & Architecture
Months 1-2Comprehensive research on blockchain fraud patterns and development of the CyBlocks architecture.
Key Activities:
- Fraud pattern analysis
- Algorithm research
- System architecture design
- Data model development
Performance Metrics
Fraud Detection Performance
The implementation of CyBlocks dramatically improved key fraud detection metrics across the board. This chart compares performance before and after implementation, showing significant reductions in false positives, false negatives, and investigation time, while improving detection speed and recovery rates.
Transaction Monitoring Trends
This chart tracks the growth in transaction volume monitored by CyBlocks over its first year of operation. Despite the exponential growth in monitored transactions, the system maintained high accuracy in flagging suspicious activities and confirming actual fraud cases, demonstrating its scalability and precision.
Platform Capabilities
Blockchain Network Coverage
CyBlocks provides comprehensive monitoring across multiple blockchain networks, with particular strength in Ethereum and Bitcoin ecosystems. This distribution represents the relative transaction volume monitored across different chains, showcasing the platform's versatility in handling various blockchain protocols and smart contract environments.
Fraud Detection Capabilities
This radar chart illustrates CyBlocks' detection capabilities across various types of blockchain fraud compared to traditional solutions. The platform demonstrates superior performance in all categories, with particular strength in detecting smart contract exploits, money laundering, and ransomware payments—areas where conventional systems struggle the most.
Risk Analysis
Transaction Risk Assessment
CyBlocks employs a sophisticated risk assessment model that evaluates transactions based on anomaly scores and risk factors. This scatter plot visualizes how the system categorizes transactions, with clear separation between high-risk activities (potential fraud) and legitimate transactions. This precise classification enables security teams to focus their investigations on the most suspicious activities.
Fraud Detection
Fraud Type Analysis
CyBlocks has detected and classified thousands of fraudulent activities across multiple categories. This interactive breakdown shows the types of fraud identified during the first year of operation, highlighting the platform's comprehensive coverage of blockchain threat vectors.
Smart Contract Exploits
- Reentrancy Attacks128 detected
Exploits allowing attackers to withdraw funds multiple times before balance updates
- Flash Loan Attacks87 detected
Manipulating market prices through uncollateralized loans within a single transaction
- Oracle Manipulation64 detected
Tampering with price feed data to exploit DeFi protocols
- Access Control Flaws112 detected
Exploiting insufficient permission checks in contract functions
- Integer Overflow/Underflow93 detected
Manipulating numeric variables to bypass balance checks