Machine Learning-Driven Anomaly Detection in Blockchain Transactions for High-Security Digital Banking
Abstract
This research paper proposes a robust machine learning-driven anomaly detection framework tailored for blockchain-based digital banking systems. The increasing volume and complexity of digital financial transactions, coupled with the transparency and immutability of blockchain, necessitate advanced techniques for identifying anomalous behaviors that may indicate fraud, cyberattacks, or operational errors. The proposed approach combines supervised and unsupervised machine learning models to analyze transaction patterns in real time and flag anomalies. It integrates deep neural networks (DNN), isolation forests, and ensemble learning techniques with a blockchain transaction audit layer for enhanced interpretability. The system is designed for scalability, low latency, and high detection accuracy. Experimental evaluation on synthetic and real-world transaction datasets demonstrates superior performance over traditional rule-based and statistical approaches.