[1] ADAMS C.Learning the lessons of WannaCry[J].Computer Fraud & Security, 2018(9):6-9. [2] PHAM T, LEE S.Anomaly detection in Bitcoin network using unsupervised learning methods[EB/OL].[2020-11-05].https://arxiv.org/pdf/1611.03941v2.pdf. [3] RACHANA K, MONICA C.Anomaly detection in blockchain using clustering protocol[J].International Journal of Engineering Research in Computer Science and Engineering, 2017, 4(12):12-23. [4] THAI P, STEVEN L.Anomaly detection in the Bitcoin system-a network perspective[EB/OL].[2020-11-05].https://arxiv.org/pdf/1611.03942v2.pdf. [5] WU J, LIU J, CHEN W, et al.Detecting mixing services via mining Bitcoin transaction network with hybrid motifs[EB/OL].[2020-11-05].https://arxiv.org/pdf/2001.05233.pdf. [6] WEBER M, DOMENICONI G, CHEN J, et al.Anti-money laundering in Bitcoin:experimenting with graph convolutional networks for financial forensics[EB/OL].[2020-11-05].https://arxiv.org/pdf/1908.02591v1.pdf. [7] HU Y, SENEVIRATNE S, THILAKARATHNA K, et al.Characterizing and detecting money laundering activities on the Bitcoin network[EB/OL].[2020-11-05].https://arxiv.org/pdf/1912.12060v1.pdf. [8] TOYODA K, OHTSUKI T, MATHIOPOULOS P T.Multi-class Bitcoin-enabled service identification based on transaction history summarization[C]//Proceedings of IEEE International Conference on Blockchain.Washington D.C., USA:IEEE Press, 2018:1153-1160. [9] NERURKAR P, BHIRUD S, PATEL D, et al.Supervised learning model for identifying illegal activities in Bitcoin[J].Applied Intelligence, 2020, 6:1-20. [10] PRANAV N, YANN B, ROMARIC L, et al.Detecting illicit entities in Bitcoin using supervised learning of ensemble decision trees[EB/OL].[2020-11-05].https://hal-imt-atlantique.archives-ouvertes.fr/hal-02952081/document. [11] MASSIMO B, BARBARA P, SERGIO S.Data mining for detecting Bitcoin ponzi schemes[C]//Proceeding of Crypto Valley Conference on Blockchain Technology.Washington D.C., USA:IEEE Press, 2018:75-84. [12] YIN H H S, LANGENHELDT K, HARLEV M, et al.Regulating cryptocurrencies:a supervised machine learning approach to de-anonymizing the Bitcoin blockchain[J].Journal of Management Information Systems, 2019, 36(1):37-73. [13] HARLEV M A, YIN H S, LANGENHELDT K C, et al.Breaking bad:de-anonymising entity types on the Bitcoin blockchain using supervised machine learning[EB/OL].[2020-11-05].https://scholarspace.manoa.hawaii.edu/bitstream/10125/50331/1/paper0444.pdf. [14] RANSHOUS S, JOSLYN C A, KREYLING S, et al.Exchange pattern mining in the Bitcoin transaction directed hypergraph[C]//Proceedings of International Conference on Financial Cryptography and Data Security.Berlin, Germany:Springer, 2017:248-263. [15] JOURDAN M, BLANDIN S, WYNTER L, et al.Characterizing entities in the Bitcoin blockchain[EB/OL].[2020-11-05].https://arxiv.org/pdf/1810.11956.pdf. [16] REID F, HARRIGAN M.An analysis of anonymity in the Bitcoin system[EB/OL].[2020-11-05].https://arxiv.org/PS_cache/arxiv/pdf/1107/1107.4524v1.pdf. [17] JAWAHERI H A, SABAH M A, BOSHMAF Y, et al.When a small leak sinks a great ship:deanonymizing tor hidden service users through Bitcoin transactions analysis[EB/OL].[2020-11-05].https://arxiv.org/pdf/1801.07501.pdf. [18] RON D, SHAMIR A.Quantitative analysis of the full Bitcoin transaction graph[C]//Proceedings of International Conference on Financial Cryptography and Data Security.Berlin, Germany:Springer, 2013:6-24. [19] MILO R, SHEN-ORR S, ITZKOVITZ S, et al.Network motifs:simple building blocks of complex networks[J].Science, 2002, 298(5594):824-827. [20] JOURDAN M, BLANDIN S, WYNTER L, et al.A probabilistic model of the Bitcoin blockchain[EB/OL].[2020-11-05].https://arxiv.org/pdf/1812.05451.pdf. [21] HE X R, PAN J F, OU J, et al.Practical lessons from predicting clicks on ads at Facebook[EB/OL].[2020-11-05].http://www0.cs.ucl.ac.uk/staff/w.zhang/pubapers/adkdd_2014_camera_ready_junfeng.pdf. |