Reinforcement Learning-Enhanced Adaptive Blockchain Oracles for Secure and Efficient Data Aggregation

Abstract

Blockchain oracles are the intermediaries between their smart contracts and the environment. Thus, they put the smart contracts at risk of manipulation, adversarial attacks, or unreliable data. Therefore, undoubtedly a considerable security challenge. Classical oracle mechanisms usually do not have adaptive filtering mechanisms to filter unreliable information or data. This in itself makes them easily attackable. The herein proposed system is an Adaptive Reinforcement Learning-Based Fraud-Resistant Oracle for strengthening the Oracle data protection against any sort of manipulation or direct attacks. This model is an adaptive dynamic one that monitors a multitude of data sources, trust scores being awarded for historical accuracy. Using real-time cryptocurrency price data acquisition, the proposed system applies adaptive trust evaluation and rejects manipulated inputs. Experimental results demonstrate that the RL-based mechanism provides higher fraud resistance and accuracy than conventional oracles, strengthening blockchain-based financial systems, DeFi applications, and decentralized decision-making.