Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction

TL;DR

This paper introduces H-BRP, a hybrid model for predicting blockchain peer reliability to help users connect with trustworthy peers, reducing resource waste and financial loss.

Contribution

The paper presents a novel hybrid prediction model that evaluates and predicts blockchain peer reliability personalized for users, supported by large-scale real-world experiments.

Findings

H-BRP outperforms existing approaches in accuracy.

Large-scale dataset with 2 million test cases is provided.

Reliable peer prediction improves resource efficiency and security.

Abstract

Blockchain and blockchain-based decentralized applications are attracting increasing attentions recently. In public blockchain systems, users usually connect to third-party peers or run a peer to join the P2P blockchain network. However, connecting to unreliable blockchain peers will make users waste resources and even lose millions of dollars of cryptocurrencies. In order to select the reliable blockchain peers, it is urgently needed to evaluate and predict the reliability of them. Faced with this problem, we propose H-BRP, Hybrid Blockchain Reliability Prediction model to extract the blockchain reliability factors then make personalized prediction for each user. Large-scale real-world experiments are conducted on 100 blockchain requesters and 200 blockchain peers. The implement and dataset of 2,000,000 test cases are released. The experimental results show that the proposed model obtains better accuracy than other approaches.