Enhancing Digital Financial Security with LSTM and Blockchain Technology

Abstract

The growing dependence on digital financial and banking transactions has brought about a significant focus on implementing strong security protocols. Blockchain technology has proved itself throughout the years to be a reliable solution upon which transactions can safely take place. This study explores the use of blockchain technology, specifically Ethereum Classic (ETC), to enhance the security of digital financial and banking transactions. The aim is to develop a system using an LSTM model to predict and detect anomalies in transaction data. The proposed LSTM model was trained before being tested and the results prove that the proposed model can effectively enhance the security, especially when compared to other studies in the same domain. The proposed model achieved a prediction accuracy of 99.5%, demonstrating its effectiveness in enhancing security by preventing overfitting and identifying potential threats in network activities. The results suggest significant improvements in digital transaction security, enhancing both the traceability and transparency of blockchain transactions while reducing fraud rates. Future work will extend this model's applicability to larger-scale decentralized finance systems.