Advanced financial security system using smart contract in private ethereum consortium blockchain with hybrid optimization strategy
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a deep learning-based smart contract on a private Ethereum blockchain to enhance financial security. The novel system improves transparency and settlement times compared to traditional stock markets.
Area Of Science
- Computer Science
- Financial Technology
- Blockchain Technology
Background
- Traditional stock markets face challenges with intermediaries, leading to weak transparency, delayed settlements, and single points of failure.
- Blockchain technology offers a decentralized, secure ledger system that can mitigate these issues.
Purpose Of The Study
- To develop a secure financial system using deep learning-based smart contracts on a private Ethereum consortium blockchain (PEC-BC).
- To improve transparency and efficiency in financial settlements within the stock market.
Main Methods
- Data collection and preprocessing.
- Utilizing the Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA) for leader block selection and parameter optimization.
- Implementing Adaptive Deep Temporal Context Networks (ADTCN) with a consensus algorithm for securing smart contracts.
Main Results
- The developed ADTCN-based financial security system demonstrated superior performance compared to conventional methods.
- The system effectively leverages deep learning and blockchain for secure financial transactions.
Conclusions
- The proposed PEC-BC system with ADTCN and DB-BOA offers a robust and efficient solution for financial security.
- This approach addresses the limitations of traditional stock market systems by enhancing security and reducing settlement times.
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