Advanced financial security system using smart contract in private ethereum consortium blockchain with hybrid optimization strategy - S. C. Prabanand; M. S. Thanabal (Scientific Reports, 2025)
Abstract In traditional stock market, the global processing framework shares the data to various mediators like financial and government institutions. The institutional firms needs to handle with large number of data in the system and cooperates with others to provide the trades on the stock exchange platform, and consistently buy-sell orders pass through various parties before settlement. It involves a complex chain of intermediaries, has several drawbacks like weak transparency, long lead times for financial settlements, and a single point of failure. Blockchain (BC) computer node network securely shares the common ledger without intermediaries. This paper uses a deep learning-based Smart contract in the private ethereum consortium blockchain (PEC-BC) to provide financial security. First, the data is collected. Then it is given to the next stage. The Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA) is used to choose the leader block. Further, the selected new leader block is used in the Adaptive Deep Temporal Context Networks (ADTCN) with a consensus algorithm to make secured smart contracts. Here, the parameters are optimized by DB-BOA. The developed ADTCN-based financial security system was compared with other conventional methods, and algorithms performed well.