Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction
This addresses the issue of selecting reliable peers in public blockchain networks for users, but it appears incremental as it builds on existing prediction methods.
The paper tackles the problem of unreliable blockchain peers causing resource waste and financial loss by proposing H-BRP, a hybrid model for predicting peer reliability, which achieves better accuracy in large-scale experiments with 100 requesters and 200 peers.
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.