Machine Learning Scam Address Detection in Cryptocurrency
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Solution Overview
Problem
The anonymity of cryptocurrency transactions makes it difficult to manually identify scam addresses, leading to challenges in tracking and preventing criminal activities, as existing methods lack efficiency in processing large amounts of data.
Innovation Solution
A method using a machine learning model is developed to detect scam addresses by acquiring and analyzing information from labeled scam and benign addresses, generating feature information, and determining scam risk scores based on transaction patterns and ratios, allowing for automated identification of potentially fraudulent addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of cryptocurrency transactions is used, then detection accuracy may be maintained, but processing efficiency and productivity deteriorate due to massive data volume
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated machine learning system that uses algorithms to detect scam addresses. The system automatically extracts features from transaction data and applies trained models to identify fraudulent addresses, eliminating the need for manual examination of massive transaction volumes while maintaining or improving detection accuracy.
Solution Approach 2:
The machine learning model is trained using labeled scam and benign address data, enabling the system to self-improve and adapt to new scam patterns autonomously. The system continuously learns from transaction data and updates its detection capabilities without requiring manual reprogramming or intervention, allowing it to handle increasing data volumes efficiently.
2Productivity
If machine learning automation is implemented, then productivity and efficiency improve, but measurement precision and detection accuracy may worsen due to automated decision-making limitations
Solution Approach 1:
The system performs preliminary actions by collecting and labeling extensive scam and benign address data before training the machine learning model. This preparatory phase includes gathering transaction records, identifying scam patterns, and creating a comprehensive training dataset, which establishes a solid foundation for accurate automated detection and reduces false positives.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously evaluated and used to retrain and improve the machine learning model. By analyzing both correct and incorrect detections, the system learns from its mistakes and refines its detection algorithms, progressively improving accuracy while maintaining high productivity.
3Measurement precision
If comprehensive feature extraction is performed on all addresses, then detection precision improves, but processing time and loss of time increase
Solution Approach 1:
The system extracts only the most relevant features from transaction data rather than analyzing all possible attributes. It identifies and extracts key features such as transaction frequency, amount patterns, and address relationship metrics that are most indicative of scam behavior, eliminating unnecessary processing of irrelevant data while maintaining detection accuracy.
Solution Approach 2:
The system performs partial feature extraction by focusing on a subset of critical features rather than comprehensive analysis of all transaction attributes. This selective approach extracts only the essential characteristics needed for scam detection, reducing processing time while maintaining sufficient precision for effective identification of fraudulent addresses.
Data Source
AI summary
The present disclosure relates to a method for detecting a scam address of cryptocurrency using a machine learning model, and the method comprises: acquiring information about scam addresses labeled as being used for a scam transaction and information about benign addresses labeled as being used for a normal transaction from a database; acquiring information about a mule address group used for money laundering on the basis of the scam address group; acquiring feature information corresponding to each of the benign addresses and the addresses included in the scam address group or the mule address group on the basis of at least one of the information about the benign addresses, the information about the scam address group, and the information about the mule address group; and generating a machine learning model by machine learning of the feature information corresponding to each of the addresses and label information corresponding to each of the addresses.