Blockchain Transaction Fraud Detection Using Deep Learning Features
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Solution Overview
Problem
Conventional fraud detection systems for cryptocurrencies face limitations in updating to continuously changing fraud patterns without human intervention and are inadequate for the unique data types present in blockchain transactions.
Innovation Solution
A deep learning-based method that preprocesses transaction data, extracts feature vectors, and uses a supervised learning approach to improve fraud detection by generating and oversampling data sets, employing convolutional neural networks and normalization techniques to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional fraud detection systems are used for cryptocurrency transactions, then basic detection functionality is provided, but the systems cannot update to continuously changing fraud patterns without human intervention and are inadequate for unique blockchain data types
Solution Approach 1:
The deep learning model performs automated feature extraction and classification of fraud patterns without requiring manual intervention. The system self-updates by learning from new transaction data, automatically adapting to evolving fraud techniques while maintaining high automation levels throughout the detection process
Solution Approach 2:
The system dynamically adapts to changing fraud patterns through continuous learning from new blockchain transaction data. The deep learning model's parameters are updated automatically based on emerging fraud patterns, enabling the system to remain effective against continuously evolving threats without manual reconfiguration
2Measurement precision
If multiple data preprocessing steps and feature extraction methods are implemented, then fraud detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex fraud detection process into distinct modules: data preprocessing module, feature extraction module, and classification module. Each module handles specific tasks independently, making the overall complex system manageable and maintainable while achieving high detection accuracy through specialized processing at each stage
Solution Approach 2:
The deep learning model serves multiple functions simultaneously: it extracts features from diverse blockchain data types, classifies fraud patterns, and adapts to new threats. This multi-functionality reduces the need for separate specialized systems, managing complexity while maintaining comprehensive detection capabilities
3Reliability
If deep learning models with multiple data sets are used, then fraud detection performance is enhanced, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and data preprocessing before the actual fraud detection classification. By preparing features in advance and organizing data into structured formats, the system reduces the computational burden during real-time detection, thereby decreasing processing time while maintaining high detection performance
Solution Approach 2:
The system replaces traditional rule-based fraud detection mechanisms with deep learning models that automatically learn patterns from data. This substitution enables parallel processing of multiple data sets and features, significantly reducing processing time compared to sequential rule-based approaches while improving detection reliability
Data Source
AI summary
The described technology relates to a deep learning based method and system for detecting abnormal cryptocurrency transaction between computing devices in a blockchain network. In once aspect, the method includes generating, at a server, a first data set, a second data set, and a third data set from the transactions of a user wallet address with at least one other user wallet address. The method may also include running a pre-learned deep learning module to extract a first feature vector, a second feature vector, and a third feature vector from the first data set, the second data set, and the third data set. The method may further include converting the first feature vector, the second feature vector, and the third feature vector into an intermediate value and comparing the intermediate value to a predetermined reference value to determine if a fraudulent transaction associated with the user wallet address has occurred.