Contract Network Forensics Using Graph-Based Fraud Detection
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Solution Overview
Problem
Current blockchain networks face challenges in organizing and tracking updates to multi-party smart contracts, which are difficult to upgrade due to their immutable nature, and are vulnerable to fraudulent transactions and complex fraud schemes involving multiple collaborating contracts.
Innovation Solution
The Value Token Transfer Protocol (VTTP) enables intra- and inter-chain value transfers across blockchain networks, supports a client-server and peer-to-peer model, and employs computational graph modeling to detect coordinated fraud by analyzing inter-contract relationships and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If smart contracts are made immutable to ensure security and trustlessness, then reliability is improved, but adaptability deteriorates making upgrades difficult
Solution Approach 1:
The patent segments the smart contract system into multiple components: immutable core contracts for security, and separate upgradeable modules or proxy contracts for adaptability. This allows the system to maintain reliability through immutable code while achieving adaptability through structured decomposition and modular replacement of specific contract components.
Solution Approach 2:
The patent introduces intermediary structures such as proxy contracts, factory contracts, or governance mechanisms that mediate between the immutable smart contract code and the need for upgrades. These intermediaries enable controlled modifications and updates without compromising the security guarantees of the original immutable contracts.
2Ease of operation
If blockchain networks use traditional transaction monitoring to detect fraud, then ease of operation is maintained, but measurement precision deteriorates failing to identify coordinated illicit activities
Solution Approach 1:
The patent transitions from traditional single-dimension transaction monitoring to multi-dimensional analysis by incorporating graph-based representations of contract relationships, temporal patterns, and network topology. This dimensional expansion enables detection of coordinated fraud schemes that operate across multiple contracts while maintaining operational simplicity through automated analysis.
Solution Approach 2:
The patent combines multiple detection methodologies into a composite fraud detection system that integrates traditional transaction monitoring with advanced graph analysis, machine learning models, and pattern recognition. This composite approach achieves high measurement precision for sophisticated fraud while preserving ease of operation through unified automated processing.
Data Source
AI summary
Systems and methods for blockchain value transfers including receiving at the server a plurality of transaction requests, each transaction request including sending and receiving user account addresses on first and second blockchain networks, a transaction value in in-network tokens, and an API request, performing a balance check procedure on each transaction request including determining if the transaction value is greater than a permitted transaction amount of the sending user account address, and either refusing the transaction or adding the transaction to an aggregate transaction record. The permitted transaction is updated for the sending and receiving addresses to reflect the transaction and a transaction including calculating a net transaction amount is initiated.