Integrating machine learning with proof-of-authority-and-association for dynamic signer selection in blockchain networks
Dong‐Seong Kim, Syamsul Rizal
Abstract
Integrating machine learning (ML) into blockchain consensus mechanisms enhances efficiency, scalability, and resilience. This study introduces the PoA 2 algorithm, an ML-enhanced Proof of Authority mechanism that optimizes signer selection for improved transaction processing. Simulations with models including Random Forest, Logistic Regression, SVM, K-Nearest Neighbors, Decision Tree, and Gradient Boosting showed significant gains. Random Forest reduced latency tenfold, achieving nearly 1000 transactions per second, with 93.33% accuracy, 100% precision, 86.67% recall, and a 92.86% F1-score. These results demonstrate ML’s potential to enhance blockchain performance, making hybrid blockchain-ML solutions a promising research direction.