Decoy Blockchain Verification for Card-Not-Present Transactions
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Solution Overview
Problem
Existing online transaction verification methods, such as password, one-time password (OTP), multi-factor authentication, and blockchain, fail to prevent unauthorized use of debit or credit cards in card not present (CNP) transactions, leading to fraud that is only detected after the fact.
Innovation Solution
A system utilizing a blockchain network and a network of smartdust sensors, paired with a deep convolutional neural network (DCNN), generates decoy transactional blocks to confuse hackers while authenticating transactions through a sensor network and historical data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional verification methods (password, OTP, multi-factor authentication) are used, then transaction processing is simple and fast, but security against unauthorized use is insufficient
Solution Approach 1:
The system performs preliminary authentication by analyzing transactional behavior patterns, device characteristics, and historical data before the transaction is finalized. This advance verification identifies potential fraud attempts early, preventing unauthorized transactions while maintaining simplicity for legitimate users.
Solution Approach 2:
The system creates a digital twin or replica of the user's transactional behavior profile based on historical data. This behavioral copy is then compared against new transactions to detect anomalies, providing enhanced security without requiring complex real-time verification steps from the user.
2Reliability
If blockchain technology is implemented for transaction verification, then security and transparency are improved, but transaction processing time increases
Solution Approach 1:
The system pre-analyzes and establishes baseline transactional behavior patterns during off-peak times or using historical data. This preliminary preparation enables faster real-time verification by comparing new transactions against pre-computed behavioral profiles, reducing processing time while maintaining security.
Solution Approach 2:
The system applies different verification depths to different transactions based on their risk profiles. Low-risk transactions following normal behavioral patterns receive minimal verification, while unusual transactions trigger more intensive checks. This localized approach optimizes processing time for each transaction type.
Data Source
AI summary
The present disclosure provides a security method, a computing platform, and a system for enhanced online transaction security. The method includes receiving transactional information from a user, and distributing the transactional information over a sensor network of the computing platform. The method also includes generating a decoy transactional block that imitates the transactional information within a blockchain network of the computing platform. The method further includes displaying an association page for the user to enter a verification code and deleting the decoy transactional block from the blockchain network based on determining that the transactional information is authentic.