Blockchain Transaction Graph Anomaly Detection With GNN Analysis
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Solution Overview
Problem
Existing blockchain systems face challenges in detecting fraudulent activities and anomalies due to the difficulty in altering established blocks, which can introduce fraudulent data, necessitating improved methods for anomaly detection.
Innovation Solution
An AI-powered system utilizing a graph neural network (GNN) and graphics processing units (GPUs) to analyze block transactions graphs, identifying irregular patterns and generating alerts for anomalies such as phishing or fraud by extracting graph parameters, generating statistical approximations, and training models to classify anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain blocks are made immutable to prevent fraud, then security is improved, but ability to detect fraudulent activities in existing blocks deteriorates
Solution Approach 1:
The system performs preliminary analysis by extracting graph parameters and generating statistical approximations of block transactions before final validation. This allows the system to detect anomalies in advance while blocks are still being processed, enabling detection without compromising the immutability of confirmed blocks.
Solution Approach 2:
The patent introduces an intermediary analysis layer using graph neural networks and statistical models that sits between the blockchain transactions and the final validation process. This intermediary layer analyzes transaction patterns and graph structures to identify fraudulent activities without altering the underlying blockchain data.
2Ease of manufacture
If traditional anomaly detection methods are used on blockchain data, then implementation simplicity is maintained, but detection accuracy and real-time capability deteriorate
Solution Approach 1:
The patent replaces traditional mechanical or rule-based anomaly detection methods with AI-powered graph neural networks and statistical approximations. This substitution enables the system to achieve high detection accuracy and real-time processing capability while maintaining relative simplicity through automated machine learning processes.
Solution Approach 2:
The system changes the parameters of analysis by transforming block transaction data into graph representations with extracted parameters. This parameter transformation enables more sophisticated anomaly detection using statistical approximations and machine learning models, significantly improving detection accuracy compared to traditional methods.
3Measurement precision
If comprehensive graph analysis is performed on all block transactions, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts key graph parameters from the complete block transaction graphs, such as degree distribution, clustering coefficients, and other statistical features. By extracting only the essential parameters rather than analyzing every transaction detail, the system achieves high detection precision while significantly reducing processing time and computational requirements.
Solution Approach 2:
The system performs partial graph analysis by focusing on specific graph parameters and statistical approximations that are most indicative of fraudulent activities. This selective analysis approach provides sufficient detection precision without the need to exhaustively analyze all aspects of the graph structure, thereby reducing processing time.
Data Source
AI summary
Systems and methods to identify blockchain anomalies include an AI tool comprising a processor, a GPU, and a GNN model to extract graph parameters from a block transactions graph of a blockchain block, generate statistical approximations of the graph based on the graph parameters, compare the statistical approximations to at least one anomaly threshold, detect an irregular graph pattern in the graph when the statistical approximations exceed the at least one anomaly threshold, identify via the GNN model an anomaly within the block transactions graph based on the irregular graph pattern, generate via the GPU an address graph based on the block transactions graph when the anomaly is identified to display one or more addresses associated with the anomaly, and generate an alert when the anomaly is identified.