BNS: A Detection System to Find Nodes in the Bitcoin Network

Ruiguang Li (), Liehuang Zhu, Chao Li, Fudong Wu () and Dawei Xu
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Ruiguang Li: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Liehuang Zhu: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Chao Li: National Computer Network Emergency Response Technical Team/Coordination Center, Beijing 100029, China
Fudong Wu: School of Cyberspace Science and Technology, Beihang University, Beijing 100191, China
Dawei Xu: School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Mathematics, 2023, vol. 11, issue 24, 1-14

Abstract: Bitcoin was launched over a decade ago and has made an increasing impact on the world’s financial order, which has attracted the attention of researchers all over the world. The Bitcoin system runs on a dynamic P2P network, containing tens of thousands of nodes, including reachable nodes and unreachable nodes. In this article, a detection system, BNS (Bitcoin Network Sniffer), which could collect as many Bitcoin nodes as possible is proposed. For reachable nodes, the authors designed an algorithm, BRF (Bitcoin Reachable-Nodes Finding), based on node activity evaluation which reduces the nodes to be detected and greatly shortens the detection time. For unreachable nodes, the authors trained a decision tree model, BUF (Bitcoin Unreachable-Nodes Finding), to identify unreachable nodes based on attribute features from a large number of node addresses. Experiments showed that BNS discovered an average of 1093 more reachable nodes (6.4%) and 662 more unreachable nodes (2.3%) than the well-known website “Bitnodes” per day. It showed better performance in total nodes and efficiency. Based on the experimental results, the authors analyzed the real network size, node “churn”, and geographical distribution.

Keywords: Bitcoin; reachable nodes; unreachable nodes; node activity; decision tree model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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