Bitcoin Network Size Estimation Based on Coupon Collection Model

Bitcoin was originally proposed in 2009 and exists as a p2p digital currency that can be exchanged for currency in most countries and is uncontrollable. Moreover, the bitcoin network is unstable, so it is imperative to study the security of bitcoin, and estimating its network size is one of the basic and important links. Based on the coupon collector model, this paper uses active measurement to estimate the size of the Bitcoin network. Experiments show that the model can achieve an average coverage of 88%, so the model can be used to estimate the network size. At the end of the paper, the problems and improvement ideas of the model are put forward, and this is the direction and focus of future research.

[1]  Cao Jia Research about size estimation methods in P2P network , 2008 .

[2]  Yan Jia,et al.  Bidirectional self-adaptive resampling in internet of things big data learning , 2018, Multimedia Tools and Applications.

[3]  Feng Jiang,et al.  A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices , 2018, Wirel. Commun. Mob. Comput..

[4]  Ke Xu,et al.  Understanding peer distribution in the global internet , 2010, IEEE Network.

[5]  Maximilian Michel,et al.  Characterization of BitTorrent swarms and their distribution in the Internet , 2011, Comput. Networks.

[6]  Mohsen Guizani,et al.  A data-driven method for future Internet route decision modeling , 2019, Future Gener. Comput. Syst..

[7]  Mohan Li,et al.  Answering the Min-Cost Quality-Aware Query on Multi-sources in Sensor-Cloud Systems , 2018, SpaCCS.

[8]  Xianzhi Wang,et al.  Trust architecture and reputation evaluation for internet of things , 2018, J. Ambient Intell. Humaniz. Comput..

[9]  Jinqiao Shi,et al.  Toward a Comprehensive Insight Into the Eclipse Attacks of Tor Hidden Services , 2019, IEEE Internet of Things Journal.

[10]  Yingwu Chen,et al.  Time Optimization of Multiple Knowledge Transfers in the Big Data Environment , 2018 .

[11]  Shen Su,et al.  Automatically Traceback RDP-Based Targeted Ransomware Attacks , 2018, Wirel. Commun. Mob. Comput..

[12]  Donald F. Towsley,et al.  Neighbor discovery in wireless networks and the coupon collector's problem , 2009, MobiCom '09.

[13]  Xu Peng,et al.  Research on Measurement of Peer-to-Peer File Sharing System , 2006 .

[14]  Ning Cao,et al.  Network Security Situation Awareness Framework based on Threat Intelligence , 2018 .

[15]  Wang Yijie,et al.  Paragraph Vector Representation Based on Word to Vector and CNN Learning , 2018 .

[16]  Ming Yang,et al.  Extensive analysis and large-scale empirical evaluation of tor bridge discovery , 2012, 2012 Proceedings IEEE INFOCOM.

[17]  Zhaoquan Gu,et al.  Automatic Non-Taxonomic Relation Extraction from Big Data in Smart City , 2018, IEEE Access.

[18]  Yue Gao,et al.  Large-scale discovery and empirical analysis for I2P eepSites , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[19]  Peter Neal,et al.  The Generalised Coupon Collector Problem , 2008, Journal of Applied Probability.

[20]  Shen Su,et al.  A Privacy Preserving Scheme for Nearest Neighbor Query , 2018, Sensors.

[21]  Xiaoxia Yin,et al.  A Real-Time Correlation of Host-Level Events in Cyber Range Service for Smart Campus , 2018, IEEE Access.

[22]  Bo Gao,et al.  Fog Computing-Assisted Energy-Efficient Resource Allocation for High-Mobility MIMO-OFDMA Networks , 2018, Wirel. Commun. Mob. Comput..

[23]  Qi Cui Identifying materials of photographic images and photorealistic computer generated graphics based on deep CNNs , 2018 .