轻量级比特币交易溯源机制
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摘要
比特币技术发展迅速,交易规模逐步攀升,引起国内外广泛关注.比特币技术具备去中心化和匿名化特征,使得比特币交易者的身份难以识别,为不法行为(如毒品交易、比特币勒索病毒等)提供了隐匿空间.该文提出了一种针对比特币交易的溯源机制,能够追踪交易信息在网络层的传播路径,从而将交易中的匿名比特币地址和发起交易节点的IP地址相关联.通过设计一种基于主动嗅探的邻居节点识别方法,溯源机制支持轻量级监测,而且相比现有溯源技术具有更好的实用性.文中针对比特币系统开发了溯源程序,从有效性、准确率、适用范围等方面对其进行测试与分析评估.实验结果表明,比特币网络中有69.9%的服务器节点适用于这种溯源机制,能够获得召回率50%、准确率31.25%的溯源精度,优于现有的交易溯源方法,具有较强的实践意义和使用价值.
The rapid development of Bitcoin technology and the growing scale of Bitcoin transactions have drawn wide attention at home and abroad.Whereas,Bitcoin is often used by terrorists and criminals attracted to the anonymity of the currency,such as all deals on Silk Road were made in Bitcoin.Therefore,it is essential to supervise Bitcoin and track the source transaction when necessary.However,as Bitcoin technology has the characteristics of de-centralization,traditional financial supervision means cannot provide effective supervision.Philip Koshy et al.found some special trading patterns for originating node by analyzing the propagation law of currency transactions in the network layer,but the proportion of special deals is less than 9%.Alex Biryukov et al.take advantages of the information of neighbor nodes of Bitcoin peer to locate the originating node.This approach improves fault tolerance and accuracy(experiment shows the accuracy of 11%),but requires constantly sending information to all nodes,which can cause network congestion.There are also some methods of transaction data analysis.However,they usually only get the relationship between the addresses,but cannot directly obtain the corresponding identity information of the address.Therefore,it is necessary to design a new transaction tracking mechanism for Bitcoin architecture,which can detect with fewer resources and has higher tracking accuracy than existingmechanisms.In this paper,we optimize the existing bitcoin transaction traceability mechanism and propose a new neighbor node identification scheme based on active sniffing.Our scheme supports lightweight transaction traceability and has better traceability than existing schemes.In addition,by designing a matching value optimization method based on multiple detection,the traceability mechanism can gradually improve the traceability results through continuous monitoring and improve the traceability accuracy.The main contributions of this paper include three parts:at first,we designs a practical Bitcoin transaction tracking mechanism that can track the transmission of bitcoin transactions under the public Bitcoin network and associate the anonymous bitcoin transaction with the IP address of the transaction originating node.Secondly,for the first time,we propose a new method for neighbor node detection based on active sniffing,which can infer the neighbor nodes of a specific node by sending probe information.This method can obtain the topology information of any server node with less resources.Finally,we developed a prototype system for traceability mechanisms and tested the efficiency and accuracy on public Bitcoin network.The experiment results demonstrate that 69.9% of the backbone nodes in the Bitcoin network are suitable for the proposed tracing mechanism,with traceability recall rate of 50% and accuracy of31.25%,which is superior to the current tracing methods and of great importance in practice.The proposed traceability mechanism can trace the transactions in Bitcoin networks and identify the transactions created by specific server nodes which can help to track down criminals who maliciously use bitcoin technology to deter Bitcoin-based crimes.Moreover,the traceability mechanism of this article is also applicable to altcoin based on Bitcoin code and other digital currencies based on Blockchain technology,and has a wide range of application scenarios.
The rapid development of Bitcoin technology and the growing scale of Bitcoin transactions have drawn wide attention at home and abroad.Whereas,Bitcoin is often used by terrorists and criminals attracted to the anonymity of the currency,such as all deals on Silk Road were made in Bitcoin.Therefore,it is essential to supervise Bitcoin and track the source transaction when necessary.However,as Bitcoin technology has the characteristics of de-centralization,traditional financial supervision means cannot provide effective supervision.Philip Koshy et al.found some special trading patterns for originating node by analyzing the propagation law of currency transactions in the network layer,but the proportion of special deals is less than 9%.Alex Biryukov et al.take advantages of the information of neighbor nodes of Bitcoin peer to locate the originating node.This approach improves fault tolerance and accuracy(experiment shows the accuracy of 11%),but requires constantly sending information to all nodes,which can cause network congestion.There are also some methods of transaction data analysis.However,they usually only get the relationship between the addresses,but cannot directly obtain the corresponding identity information of the address.Therefore,it is necessary to design a new transaction tracking mechanism for Bitcoin architecture,which can detect with fewer resources and has higher tracking accuracy than existingmechanisms.In this paper,we optimize the existing bitcoin transaction traceability mechanism and propose a new neighbor node identification scheme based on active sniffing.Our scheme supports lightweight transaction traceability and has better traceability than existing schemes.In addition,by designing a matching value optimization method based on multiple detection,the traceability mechanism can gradually improve the traceability results through continuous monitoring and improve the traceability accuracy.The main contributions of this paper include three parts:at first,we designs a practical Bitcoin transaction tracking mechanism that can track the transmission of bitcoin transactions under the public Bitcoin network and associate the anonymous bitcoin transaction with the IP address of the transaction originating node.Secondly,for the first time,we propose a new method for neighbor node detection based on active sniffing,which can infer the neighbor nodes of a specific node by sending probe information.This method can obtain the topology information of any server node with less resources.Finally,we developed a prototype system for traceability mechanisms and tested the efficiency and accuracy on public Bitcoin network.The experiment results demonstrate that 69.9% of the backbone nodes in the Bitcoin network are suitable for the proposed tracing mechanism,with traceability recall rate of 50% and accuracy of31.25%,which is superior to the current tracing methods and of great importance in practice.The proposed traceability mechanism can trace the transactions in Bitcoin networks and identify the transactions created by specific server nodes which can help to track down criminals who maliciously use bitcoin technology to deter Bitcoin-based crimes.Moreover,the traceability mechanism of this article is also applicable to altcoin based on Bitcoin code and other digital currencies based on Blockchain technology,and has a wide range of application scenarios.
引文
[1]Yuan Yong,Wang Fei-Yue.Blockchain:The state of the art and future trends.Acta Automatica Sinica,2016,42(4):481-494(in Chinese)(袁勇,王飞跃.区块链技术发展现状与展望.自动化学报,2016,42(4):481-494)
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[3]Luo Qiang,Zhang Rui.Bitcoin.Beijing:China Machine Press,2014(in Chinese)(罗强,张睿.比特币.北京:机械工业出版社,2014)
[4]Zhao Kuo,Xing Yong-Heng.Security survey of Internet of Things driven by blockchain technology.Netinfo Security,2017,(5):1-6(in Chinese)(赵阔,邢永恒.区块链技术驱动下的物联网安全研究综述.信息网络安全,2017,(5):1-6)
[5]Moser M,Bohme R,Breuker D.An inquiry into money laundering tools in the Bitcoin ecosystem//Proceedings of the eCrime Researcher Symposium.Birmingham,England,2014:1-14
[6]Bonneau J,Narayanan A,Miller A,et al.Mixcoin:Anonymity for Bitcoin with accountable mixes//Proceedings of the 18th International Conference on Financial Cryptography and Data Security Financial.Christ Church,Barbados,2014:486-504
[7]Ruffing T,Moreno-Sanchez P,Kate A.CoinShuffle:Practical decentralized coin mixing for Bitcoin//Proceedings of the 19th European Symposium on Research in Computer Security.Wroclaw,Poland,2014:345-364
[8]Bissias G,Ozisik A P,Levine B N,et al.Sybil-resistant mixing for Bitcoin//Proceedings of the 2015ACM Workshop on Privacy in the Electronic Society.New York,USA,2014:149-158
[9]Ziegeldorf J H,Grossmann F,Henze M,et al.CoinParty:Secure multi-party mixing of Bitcoins//Proceedings of the 5th ACM Conference on Data and Application Security and Privacy.San Antonio,USA,2015:75-86
[10]Valenta L,Rowan B.Blindcoin:Blinded,Accountable Mixes for Bitcoin.Financial Cryptography and Data Security.Berlin,Germany:Springer,2015:112-126
[11]Garay J,Kiayias A,Leonardos N.The Bitcoin backbone protocol with Chains of Variable difficulty//Proceedings of the 37th International Cryptology Conference.Santa Barbara,USA,2017:291-323
[12]Bonneau J,Miller A,Clark J,et al.SoK:Research perspectives and challenges for Bitcoin and cryptocurrencies//Proceedings of the 36th IEEE Symposium on Security and Privacy.California,USA,2015:104-121
[13]Zohar A.Bitcoin:Under the hood.Communications of the ACM,2015,58(9):104-113
[14]Zhu Lie-Huang,Gao Feng,Shen Meng,et al.Survey on privacy preserving techniques for blockchain technology.Journal of Computer Research and Development,2017,54(10):2170-2186(in Chinese)(祝烈煌,高峰,沈蒙等.区块链隐私保护研究综述.计算机研究与发展,2017,54(10):2170-2186)
[15]Androulaki E,Karame G O,Roeschlin M,et al.Evaluating user privacy in Bitcoin//Proceedings of the International Conference on Financial Cryptography and Data Security.Okinawa,Japan,2013:34-51
[16]Monaco J V.Identifying Bitcoin users by transaction behavior//Proceedings of the 2015International Society for Optics and Photonics Defense,Security,and Sensing.Baltimore,USA,2015:33-47
[17]Gervais A,Ritzdorf H,Karame G O,et al.Tampering with the delivery of blocks and transactions in Bitcoin//Proceedings of the 22nd ACM Conference on Computer and Communications Security.Colorado,USA,2015:692-705
[18]Huang Bu-Tian,Liu Zheng-Guang,Chen Jian-Hai,et al.Behavior pattern clustering in blockchain networks.Multimedia Tools&Applications,2017,76(19):20099-20110
[19]Biryukov A,Khovratovich D,Pustogarov I.Deanonymisation of clients in Bitcoin P2Pnetwork//Proceedings of the 21st ACM Conference on Computer and Communications Security.New York,USA,2014:15-29
[20]Reid F,Harrigan M.An analysis of anonymity in the Bitcoin system//Proceedings of the 3rd International Conference on Privacy,Security,Risk and Trust.Massachusetts,USA,2011:1318-1326
[21]Liao K,Zhao Z,Doupe A,et al.Behind closed doors:Measurement and analysis of CryptoLocker ransoms in Bitcoin//Proceedings of the Symposium on Electronic Crime Research.Toronto,Canada,2016:1-13
[22]Meiklejohn S,Pomarole M,Jordan G,et al.A fistful of Bitcoins:Characterizing payments among men with no names//Proceedings of the Conference on Internet Measurement.Barcelona,Spain,2013:127-140
[23]Zhao C.Graph-Based Forensic Investigation of Bitcoin Transactions[M.S.dissertation].Iowa State University,Iowa,2014
[24]Koshy P,Koshy D,Mcdaniel P.An analysis of anonymity in Bitcoin using P2P network traffic.Financial Cryptography and Data Security.Berlin,Germany:Springer,2014:469-485
[25]Ivan P.Deanonymisation Techniques for Tor and Bitcoin[Ph.D.dissertation].University of Luxembourg,Luxembourg,2015
[26]Sasson E B,Chiesa A,Garman C,et al.Zerocash:Decentralized anonymous payments from Bitcoin//Proceedings of the35th IEEE Symposium on Security and Privacy.California,USA,2014:459-474
[27]Dinh T T A,Wang J,Chen G,et al.BLOCKBENCH:A framework for analyzing private blockchains//Proceedings of the 2017 ACM International Conference on Management of Data.Chicago,USA,2017:1085-1100
(1)CoinMKTCap.CrytoCurrency Market Capitalizations.https://coinmarketcap.com/2017-10-6
(1)Kyle Torpey.You Really Should Run a Bitcoin Full Node:Here’s Why.https://bitcoinmagazine.com/articles/youreally-should-run-full-bitcoin-node-heres-why/
(1)Kaminsky D.Black Ops of TCP/IP 2011.https://dankaminsky.com/2011/08/05/bo2k11/
(1)Bitnodes.Global bitcoin nodes distribution.https://bitnodes.21.co/
(1)Bitcoin.Bitcoin Core integration/staging tree.https://github.com/bitcoin/bitcoin
(1)Kaminsky D.Black Ops of TCP/IP 2011.https://dankaminsky.com/2011/08/05/bo2k11/
(2)Bitnodes.Global bitcoin nodes distribution.https://bitnodes.21 .co/
[2]He Pu,Yu Ge,Zhang Yan-Feng,et al.Survey on blockchain technology and its application prospect.Computer Science,2017,44(4):1-7(in Chinese)(何蒲,于戈,张岩峰等.区块链技术与应用前瞻综述.计算机科学,2017,44(4):1-7)
[3]Luo Qiang,Zhang Rui.Bitcoin.Beijing:China Machine Press,2014(in Chinese)(罗强,张睿.比特币.北京:机械工业出版社,2014)
[4]Zhao Kuo,Xing Yong-Heng.Security survey of Internet of Things driven by blockchain technology.Netinfo Security,2017,(5):1-6(in Chinese)(赵阔,邢永恒.区块链技术驱动下的物联网安全研究综述.信息网络安全,2017,(5):1-6)
[5]Moser M,Bohme R,Breuker D.An inquiry into money laundering tools in the Bitcoin ecosystem//Proceedings of the eCrime Researcher Symposium.Birmingham,England,2014:1-14
[6]Bonneau J,Narayanan A,Miller A,et al.Mixcoin:Anonymity for Bitcoin with accountable mixes//Proceedings of the 18th International Conference on Financial Cryptography and Data Security Financial.Christ Church,Barbados,2014:486-504
[7]Ruffing T,Moreno-Sanchez P,Kate A.CoinShuffle:Practical decentralized coin mixing for Bitcoin//Proceedings of the 19th European Symposium on Research in Computer Security.Wroclaw,Poland,2014:345-364
[8]Bissias G,Ozisik A P,Levine B N,et al.Sybil-resistant mixing for Bitcoin//Proceedings of the 2015ACM Workshop on Privacy in the Electronic Society.New York,USA,2014:149-158
[9]Ziegeldorf J H,Grossmann F,Henze M,et al.CoinParty:Secure multi-party mixing of Bitcoins//Proceedings of the 5th ACM Conference on Data and Application Security and Privacy.San Antonio,USA,2015:75-86
[10]Valenta L,Rowan B.Blindcoin:Blinded,Accountable Mixes for Bitcoin.Financial Cryptography and Data Security.Berlin,Germany:Springer,2015:112-126
[11]Garay J,Kiayias A,Leonardos N.The Bitcoin backbone protocol with Chains of Variable difficulty//Proceedings of the 37th International Cryptology Conference.Santa Barbara,USA,2017:291-323
[12]Bonneau J,Miller A,Clark J,et al.SoK:Research perspectives and challenges for Bitcoin and cryptocurrencies//Proceedings of the 36th IEEE Symposium on Security and Privacy.California,USA,2015:104-121
[13]Zohar A.Bitcoin:Under the hood.Communications of the ACM,2015,58(9):104-113
[14]Zhu Lie-Huang,Gao Feng,Shen Meng,et al.Survey on privacy preserving techniques for blockchain technology.Journal of Computer Research and Development,2017,54(10):2170-2186(in Chinese)(祝烈煌,高峰,沈蒙等.区块链隐私保护研究综述.计算机研究与发展,2017,54(10):2170-2186)
[15]Androulaki E,Karame G O,Roeschlin M,et al.Evaluating user privacy in Bitcoin//Proceedings of the International Conference on Financial Cryptography and Data Security.Okinawa,Japan,2013:34-51
[16]Monaco J V.Identifying Bitcoin users by transaction behavior//Proceedings of the 2015International Society for Optics and Photonics Defense,Security,and Sensing.Baltimore,USA,2015:33-47
[17]Gervais A,Ritzdorf H,Karame G O,et al.Tampering with the delivery of blocks and transactions in Bitcoin//Proceedings of the 22nd ACM Conference on Computer and Communications Security.Colorado,USA,2015:692-705
[18]Huang Bu-Tian,Liu Zheng-Guang,Chen Jian-Hai,et al.Behavior pattern clustering in blockchain networks.Multimedia Tools&Applications,2017,76(19):20099-20110
[19]Biryukov A,Khovratovich D,Pustogarov I.Deanonymisation of clients in Bitcoin P2Pnetwork//Proceedings of the 21st ACM Conference on Computer and Communications Security.New York,USA,2014:15-29
[20]Reid F,Harrigan M.An analysis of anonymity in the Bitcoin system//Proceedings of the 3rd International Conference on Privacy,Security,Risk and Trust.Massachusetts,USA,2011:1318-1326
[21]Liao K,Zhao Z,Doupe A,et al.Behind closed doors:Measurement and analysis of CryptoLocker ransoms in Bitcoin//Proceedings of the Symposium on Electronic Crime Research.Toronto,Canada,2016:1-13
[22]Meiklejohn S,Pomarole M,Jordan G,et al.A fistful of Bitcoins:Characterizing payments among men with no names//Proceedings of the Conference on Internet Measurement.Barcelona,Spain,2013:127-140
[23]Zhao C.Graph-Based Forensic Investigation of Bitcoin Transactions[M.S.dissertation].Iowa State University,Iowa,2014
[24]Koshy P,Koshy D,Mcdaniel P.An analysis of anonymity in Bitcoin using P2P network traffic.Financial Cryptography and Data Security.Berlin,Germany:Springer,2014:469-485
[25]Ivan P.Deanonymisation Techniques for Tor and Bitcoin[Ph.D.dissertation].University of Luxembourg,Luxembourg,2015
[26]Sasson E B,Chiesa A,Garman C,et al.Zerocash:Decentralized anonymous payments from Bitcoin//Proceedings of the35th IEEE Symposium on Security and Privacy.California,USA,2014:459-474
[27]Dinh T T A,Wang J,Chen G,et al.BLOCKBENCH:A framework for analyzing private blockchains//Proceedings of the 2017 ACM International Conference on Management of Data.Chicago,USA,2017:1085-1100
(1)CoinMKTCap.CrytoCurrency Market Capitalizations.https://coinmarketcap.com/2017-10-6
(1)Kyle Torpey.You Really Should Run a Bitcoin Full Node:Here’s Why.https://bitcoinmagazine.com/articles/youreally-should-run-full-bitcoin-node-heres-why/
(1)Kaminsky D.Black Ops of TCP/IP 2011.https://dankaminsky.com/2011/08/05/bo2k11/
(1)Bitnodes.Global bitcoin nodes distribution.https://bitnodes.21.co/
(1)Bitcoin.Bitcoin Core integration/staging tree.https://github.com/bitcoin/bitcoin
(1)Kaminsky D.Black Ops of TCP/IP 2011.https://dankaminsky.com/2011/08/05/bo2k11/
(2)Bitnodes.Global bitcoin nodes distribution.https://bitnodes.21 .co/