Work

Integrating machine learning with proof-of-authority-and-association for dynamic signer selection in blockchain networks

Year: 2024

Type: article

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.

Source: ICT Express

DOI: 10.1016/j.icte.2024.10.008

Authors: Dong-Seong Kim, Rizal Syamsul

Institutions: Kumoh National Institute of Technology, Gumi University, Telkom University

Language: English

Topic: Blockchain Technology Applications and Security

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500