Network Usage of Bitcoin Full Node

Essaid, Meryam; Kim, Hyeon Woo; Park, Woo Guil; Lee, Ki Young; Park, Se Jin; Ju, Hong Taek

doi:10.1109/ictc.2018.8539723

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…The selection is done by randomly picking the IP with the newer timestamp (a Bitcoin node has at most 8 output connections and 117 input connections [20]). Step 3: Node A and node B send getblock,inv,getdata,getheaders,headers [21] to each other to synchronize their data. Since the third step is not the focus of this paper, it is not introduced too much.…”

Section: Plos One (Version-specific Structure [19])mentioning

confidence: 99%

Statistical and clustering analysis of attributes of Bitcoin backbone nodes

Xu,

Gao,

Zhu

et al. 2023

PLoS ONE

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Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes’ attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality.

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Section: Plos One (Version-specific Structure [19])mentioning

confidence: 99%

Statistical and clustering analysis of attributes of Bitcoin backbone nodes

Xu,

Gao,

Zhu

et al. 2023

PLoS ONE

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“…In these four types of messages, BLOCK messages are much larger than the other three types of messages. According to the reported facts [12], this paper sets the size of an INV message as 367B, a GETDATA message as 211B, a TX message as 464B, and a BLOCK message as 3104B.…”

Section: Traffic Generation In the Bitcoin Networkmentioning

confidence: 99%

Traffic Redundancy in Blockchain Systems: The Impact of Logical and Physical Network Structures

Zhang

Liu

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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The current designs of Internet-based blockchain systems generate huge traffic redundancy, thereby reducing the efficacy of the system. This paper simulates the Bitcoin blockchain network traffic in a realistic setting and studies the impacts of both logical and physical network structure on the traffic redundancy. Firstly, we construct an Internet AS layer topology graph and map the Bitcoin nodes to the ASes. Then, Bitcoin 's neighbor discovery algorithm is used to connect Bitcoin nodes together logically. Finally, we simulate the Internet traffic generated by Bitcoin 's flood-based broadcast algorithm in the AS layer topology graph. Specifically, the traffic that a message generates is calculated by the number of ASes a message passes through times the message size. The difference between the total and effective traffic (i.e., the theoretical minimum) is considered traffic redundancy. Simulations show that traffic redundancy is positively correlated with the Bitcoin network scale and the number of peer neighbors for each node. Meanwhile, the underlying physical network topology, especially the average routing path length between the Bitcoin nodes, determines the traffic redundancy as well. Our findings imply that one can optimize the blockchain network's throughput by altering the underlying physical network, e.g., migrating from the Internet to a satellite environment.

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“…Kiayias et al [21] show how the efficiency of the information propagation is directly influenced by the network topology. Propagation delay can also be substantially affected by the number of connections that nodes have [22]. Furthermore, all unstructured P2P networks, like blockchain ones, are known to suffer from the so-called topology mismatch problem (i.e., the incoherence between logical and physical links), which causes inefficiency in the transmission of data [23,24].…”

Section: Motivationmentioning

confidence: 99%

AToM: Active topology monitoring for the bitcoin peer-to-peer network

Franzoni

Salleras

Daza

2021

Peer-to-Peer Netw. Appl.

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Over the past decade, the Bitcoin P2P network protocol has become a reference model for all modern cryptocurrencies. While nodes in this network are known, the connections among them are kept hidden, as it is commonly believed that this helps protect from deanonymization and low-level attacks. However, adversaries can bypass this limitation by inferring connections through side channels. At the same time, the lack of topology information hinders the analysis of the network, which is essential to improve efficiency and security. In this paper, we thoroughly review network-level attacks and empirically show that topology obfuscation is not an effective countermeasure. We then argue that the benefits of an open topology potentially outweigh its risks, and propose a protocol to reliably infer and monitor connections among reachable nodes of the Bitcoin network. We formally analyze our protocol and experimentally evaluate its accuracy in both trusted and untrusted settings. Results show our system has a low impact on the network, and has precision and recall are over 90% with up to 20% of malicious nodes in the network.

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Network Usage of Bitcoin Full Node

Cited by 14 publications

References 8 publications

Statistical and clustering analysis of attributes of Bitcoin backbone nodes

Statistical and clustering analysis of attributes of Bitcoin backbone nodes

Traffic Redundancy in Blockchain Systems: The Impact of Logical and Physical Network Structures

AToM: Active topology monitoring for the bitcoin peer-to-peer network

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