Proof of Learning based Block Chain with Progressive Conditional Generative Adversarial Network espoused Fake Check Scams Detection and Prevention
Keywords:
Block Chain, Progressive conditional Generative Adversarial Network, Proof of learning, Probability-Based Synthetic Minority Oversampling Technique.Abstract
The issues of fraud and other irregularities in Bitcoin network are discussed in this paper. These are typical issues with online transactions and e-banking. But fraud and anomaly detection techniques also change as the financial industry does. Additionally, blockchain technology is being presented as the safest approach to be included into finance. However, a lot of frauds are also rising annually along with these sophisticated technologies. Therefore, proposed a method proof of learning based Block Chain with Progressive conditional Generative Adversarial Network for Detecting and Preventing Fake Check Scams (BC-PCGAN-EFDB). Proof of learning based Block Chain is utilized. More specifically, a Block Chain technique based on proof of learning makes it possible to confirm a check's legitimacy without disclosing personal information about the bank's clients. The Cashing-Bank may choose to proceed with the transaction or to terminate it after this verification. Furthermore, the Block Chain technique based on proof of learning has no effect on the current bank's protocols for verifying the legitimacy of checks. In order to enhance the identification and avoidance of Fake Check Scams and reduce blockchain latency, a Progressive conditional Generative Adversarial Network (PCGAN) is suggested. Here, the BC-PCGAN-EFDB proposed approach is implemented and the performance metrics, like classification error, precision, accuracy, true positive rate, computational power, integrity, availability and Confidentiality are analyzed. The proposed method gives higher accuracy 20.76%, 15.98% and 14.78% and higher precision 23.78%, 30.98% and 15.67% when comparing with existing techniques like machine learning and block chain based efficient fraud detection mechanism (ML-BBEFDM), credit card fraud detection utilizing block chain and simulated annealing k-means algorithm (CCFD-BC-SAKA) and blockchain-based solution for detecting and preventing fake check scams (BC-DFCS), methods respectively.
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