[1] LEE S,YOON C,KANG H,et al.Cybercriminal minds:an investigative study of cryptocurrency abuses in the dark Web[C]//Network and Distributed System Security Symposium,2019.
[2] PAQUET-CLOUSTON M,HASLHOFER B,DUPONT B.Ransomware payments in the bitcoin ecosystem[J].Journal of Cybersecurity,2018,5(1).
[3] BARTOLETTI M,PES B,SERUSI S.Data mining for detecting bitcoin ponzi schemes[C]//2018 Crypto Valley Conference on Blockchain Technology(CVCBT),2018:75-84.
[4] ZHENG B,ZHU L,SHEN M,et al.Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering[J].Science China Information Sciences,2020,63(3):1-15.
[5] 付烁,徐海霞,李佩丽,等.数字货币的匿名性研究[J].计算机学报,2019,42(5):1045-1062.
FU S,XU H X,LI P L,et al.A survey on anonymity of digital currecy[J].Chinese Journal of Computers,2019,42(5):1045-1062.
[6] PHAM T,LEE S.Anomaly detection in bitcoin network using unsupervised learning methods[J].arXiv:1611.03941,2016.
[7] HIRSHMAN J,HUANG Y,MACKE S.Unsupervised approaches to detecting anomalous behavior in the bitcoin transaction network[D].Stanford University,2013.
[8] PHAM T,LEE S.Anomaly detection in the bitcoin system-a network perspective[J].arXiv:1611.03942,2016.
[9] MONAMO P,MARIVATE V,TWALA B.Unsupervised learning for robust Bitcoin fraud detection[C]//2016 Information Security for South Africa(ISSA),2016:129-134.
[10] MONAMO P M,MARIVATE V,TWALA B.A multifaceted approach to bitcoin fraud detection:global and local outliers[C]//2016 15th IEEE International Conference on Machine Learning and Applications(ICMLA),2017.
[11] 沈蒙,桑安琪,祝烈煌,等.基于动机分析的区块链数字货币异常交易行为识别方法[J].计算机学报,2021,44(1):193-208.
SHEN M,SANG A Q,ZHU L H,et al.Abnormal transaction behavior recognition based on motivation analysis in Blockchain digital currency[J].Chinese Journal of Computers,2021,44(1):193-208.
[12] 曾雅倩.机器学习在比特币反洗钱中的应用[D].武汉:华中师范大学,2020.
ZENG Y Q.Application of machine learning in Bitcoin anti-money laundering[D].Wuhan:Central China Normal University,2020.
[13] OMER S.Anomaly detection in Blockchain[D].Tampere University,2019.
[14] AGGARWAL C C.Outlier analysis[M]//Data mining.Cham:Springer,2015:237-263.
[15] AGGARWAL C C,SATHE S.Outlier ensembles:an introduction[M].[S.l.]:Springer,2017.
[16] LAZAREVIC A,KUMAR V.Feature bagging for outlier detection[C]//Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining,2005:157-166.
[17] AGGARWAL C C,SATHE S.Theoretical foundations and algorithms for outlier ensembles[J].ACM SIGKDD Explorations Newsletter,2015,17(1):24-47.
[18] RAYANA S,ZHONG W,AKOGLU L.Sequential ensemble learning for outlier detection:a bias-variance perspective[C]//2016 IEEE 16th International Conference on Data Mining(ICDM),2016:1167-1172.
[19] CAMPOS G O,ZIMEK A,MEIRA W.An unsupervised boosting strategy for outlier detection ensembles[C]//Pacific-Asia Conference on Knowledge Discovery and Data Mining.Cham:Springer,2018:564-576.
[20] ZHAO Y,NASRULLAH Z,HRYNIEWICKI M K,et al.LSCP:locally selective combination in parallel outlier ensembles[C]//Proceedings of the 2019 SIAM International Conference on Data Mining(SDM),2019:585-593.
[21] 周志华.机器学习[M].北京:清华大学出版社,2016:171-175.
ZHOU Z H.Machine learning[M].Beijing:Tsinghua University Press,2016:171-175.
[22] ZHANG R,ZHANG G,LIU L,et al.Anomaly detection in bitcoin information networks with multi-constrained meta path[J].Journal of Systems Architecture,2020,110:101829.
[23] ZHAO Y,HRYNIEWICKI M K.XGBOD:improving supervised outlier detection with unsupervised representation learning[C]//2018 International Joint Conference on Neural Networks(IJCNN),2018:1-8.