BNS: A Detection System to Find Nodes in the Bitcoin Network
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 unreach...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/24/4885 |
| _version_ | 1797380184757764096 |
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| author | Ruiguang Li Liehuang Zhu Chao Li Fudong Wu Dawei Xu |
| author_facet | Ruiguang Li Liehuang Zhu Chao Li Fudong Wu Dawei Xu |
| author_sort | Ruiguang Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T20:33:40Z |
| format | Article |
| id | doaj.art-32c0e1b009974e779e5ddb7568129c32 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T20:33:40Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-32c0e1b009974e779e5ddb7568129c322023-12-22T14:23:11ZengMDPI AGMathematics2227-73902023-12-011124488510.3390/math11244885BNS: A Detection System to Find Nodes in the Bitcoin NetworkRuiguang Li0Liehuang Zhu1Chao Li2Fudong Wu3Dawei Xu4School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaNational Computer Network Emergency Response Technical Team/Coordination Center, Beijing 100029, ChinaSchool of Cyberspace Science and Technology, Beihang University, Beijing 100191, ChinaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaBitcoin 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.https://www.mdpi.com/2227-7390/11/24/4885Bitcoinreachable nodesunreachable nodesnode activitydecision tree model |
| spellingShingle | Ruiguang Li Liehuang Zhu Chao Li Fudong Wu Dawei Xu BNS: A Detection System to Find Nodes in the Bitcoin Network Mathematics Bitcoin reachable nodes unreachable nodes node activity decision tree model |
| title | BNS: A Detection System to Find Nodes in the Bitcoin Network |
| title_full | BNS: A Detection System to Find Nodes in the Bitcoin Network |
| title_fullStr | BNS: A Detection System to Find Nodes in the Bitcoin Network |
| title_full_unstemmed | BNS: A Detection System to Find Nodes in the Bitcoin Network |
| title_short | BNS: A Detection System to Find Nodes in the Bitcoin Network |
| title_sort | bns a detection system to find nodes in the bitcoin network |
| topic | Bitcoin reachable nodes unreachable nodes node activity decision tree model |
| url | https://www.mdpi.com/2227-7390/11/24/4885 |
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