  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.
