Smart Contract Graph Forensics for Coordinated Fraud
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Solution Overview
Problem
Existing blockchain networks face challenges in organizing and tracking updates to multi-party smart contracts, which are difficult to upgrade due to immutability, and are vulnerable to fraudulent transactions and sophisticated fraud schemes orchestrated across collaborating contracts, with current fraud detection methods failing to identify such coordinated illicit activities effectively.
Innovation Solution
The Value Token Transfer Protocol (VTTP) enables intra- and inter-chain value transfers and fraud detection by modeling inter-contract relationships as computational graphs, applying analytic techniques to identify coordinated fraud, and automating responses to mitigate risks.
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 versions and instances. Instead of modifying a single immutable contract, the system creates separate contract versions (e.g., contract v1, contract v2) that can coexist. This allows the system to maintain the immutability of original contracts while enabling upgrades through deployment of new contract instances, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent implements preliminary action by embedding version identifiers and upgrade metadata into the contract deployment process from the beginning. The system pre-structures contracts with versioning information and upgrade pathways, allowing future updates without compromising the immutability of the original contract code. This preliminary structuring enables seamless transitions while maintaining security.
2Ease of operation
If traditional fraud detection methods are used to identify fraudulent transactions, then ease of operation is maintained, but measurement precision deteriorates failing to detect coordinated fraud schemes
Solution Approach 1:
The patent introduces an intermediary forensic analysis system that sits between the blockchain network and the users. This intermediary automatically performs graph-based analysis of contract relationships and transaction patterns, translating complex inter-contract interactions into detectable fraud indicators. The intermediary handles the computational complexity of detecting coordinated fraud schemes while presenting simple fraud detection capabilities to users, resolving the contradiction between ease of operation and measurement precision.
3Measurement precision
If comprehensive tracking of inter-contract relationships is implemented to detect fraud, then fraud detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual or simple rule-based fraud detection mechanisms with automated graph computation systems. The system uses computational graph theory to automatically model and analyze inter-contract relationships, substituting complex manual analysis processes with algorithmic solutions. This automation handles the complexity of tracking numerous contract relationships while providing accurate fraud detection, resolving the contradiction between measurement precision and device complexity.
Data Source
AI summary
A system and method for generating risk assessment scores for blockchain addresses and smart contracts, including constructing a graph representation having nodes representing one of a blockchain address or a smart contract address on a blockchain and edges representing a transaction between an externally-owned blockchain account on the blockchain network and a smart contract address on the blockchain network, applying pattern recognition algorithms to the graph representation to identify sub-graphs having nodes indicating relationship structures indicative of potential fraudulent activity, executing filtering that analyze transaction flows, logic patterns, and behavioral characteristics of nodes within each sub-graph, and determining a risk score for each blockchain account address and smart contract address associated with each sub-graph.


