TY - JOUR
T1 - BNS
T2 - A Detection System to Find Nodes in the Bitcoin Network
AU - Li, Ruiguang
AU - Zhu, Liehuang
AU - Li, Chao
AU - Wu, Fudong
AU - Xu, Dawei
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Bitcoin
KW - decision tree model
KW - node activity
KW - reachable nodes
KW - unreachable nodes
UR - http://www.scopus.com/inward/record.url?scp=85180433305&partnerID=8YFLogxK
U2 - 10.3390/math11244885
DO - 10.3390/math11244885
M3 - Article
AN - SCOPUS:85180433305
SN - 2227-7390
VL - 11
JO - Mathematics
JF - Mathematics
IS - 24
M1 - 4885
ER -