{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:02:30Z","timestamp":1778788950712,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811592126","type":"print"},{"value":"9789811592133","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-9213-3_8","type":"book-chapter","created":{"date-parts":[[2020,11,11]],"date-time":"2020-11-11T13:03:23Z","timestamp":1605099803000},"page":"99-111","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Identifying Illicit Addresses in Bitcoin Network"],"prefix":"10.1007","author":[{"given":"Yang","family":"Li","sequence":"first","affiliation":[]},{"given":"Yue","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Gengsheng","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"8_CR1","unstructured":"Nakamoto, S., et al.: Bitcoin: A peer-to-peer electronic cash system (2008)"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Xie, S., Dai, H., Chen, X., Wang, H.: An overview of blockchain technology: Architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 557\u2013564. IEEE (2017)","DOI":"10.1109\/BigDataCongress.2017.85"},{"issue":"4","key":"8_CR3","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1504\/IJWGS.2018.095647","volume":"14","author":"Z Zheng","year":"2018","unstructured":"Zheng, Z., Xie, S., Dai, H.-N., Chen, X., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352\u2013375 (2018)","journal-title":"Int. J. Web Grid Serv."},{"key":"8_CR4","volume-title":"Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data","author":"DLK Chuen","year":"2015","unstructured":"Chuen, D.L.K.: Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data. Academic Press, Cambridge (2015)"},{"issue":"7","key":"8_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1145\/3212998","volume":"61","author":"I Eyal","year":"2018","unstructured":"Eyal, I., Sirer, E.G.: Majority is not enough: bitcoin mining is vulnerable. Commun. ACM 61(7), 95\u2013102 (2018)","journal-title":"Commun. ACM"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-1-4614-4139-7_10","volume-title":"Security and Privacy in Social Networks","author":"F Reid","year":"2013","unstructured":"Reid, F., Harrigan, M.: An analysis of anonymity in the bitcoin system. In: Altshuler, Y., Elovici, Y., Cremers, A., Aharony, N., Pentland, A. (eds.) Security and Privacy in Social Networks, pp. 197\u2013223. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4614-4139-7_10"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Hurlburt, G.F., Bojanova, I.: Bitcoin: Benefit or curse? It Professional, 16(3), 10\u201315 (2014)","DOI":"10.1109\/MITP.2014.28"},{"issue":"5","key":"8_CR8","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1093\/rfs\/hhz015","volume":"32","author":"S Foley","year":"2019","unstructured":"Foley, S., Karlsen, J.R., Putnin\u0161, T.J.: Sex, drugs, and bitcoin: how much illegal activity is financed through cryptocurrencies? Rev. Financ. Stud. 32(5), 1798\u20131853 (2019)","journal-title":"Rev. Financ. Stud."},{"key":"8_CR9","unstructured":"Janze, C.: Are cryptocurrencies criminals best friends? Examining the co-evolution of bitcoin and darknet markets (2017)"},{"key":"8_CR10","unstructured":"Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods. arXiv preprint arXiv:1611.03941 (2016)"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Monamo, P., Marivate, V., Twala, B.: Unsupervised learning for robust bitcoin fraud detection. In: 2016 Information Security for South Africa (ISSA), pp. 129\u2013134. IEEE (2016)","DOI":"10.1109\/ISSA.2016.7802939"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Lin, Y.-J., Wu, P.-W., Hsu, C.-H., Tu, I-P., Liao, S.: An evaluation of bitcoin address classification based on transaction history summarization. In: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 302\u2013310. IEEE (2019)","DOI":"10.1109\/BLOC.2019.8751410"},{"key":"8_CR13","unstructured":"Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019)"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting bitcoin Ponzi schemes. In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 75\u201384. IEEE (2018)","DOI":"10.1109\/CVCBT.2018.00014"},{"key":"8_CR15","doi-asserted-by":"publisher","first-page":"74835","DOI":"10.1109\/ACCESS.2019.2921087","volume":"7","author":"K Toyoda","year":"2019","unstructured":"Toyoda, K., Takis Mathiopoulos, P., Ohtsuki, T.: A novel methodology for hyip operators\u2019 bitcoin addresses identification. IEEE Access 7, 74835\u201374848 (2019)","journal-title":"IEEE Access"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Liao, K., Zhao, Z., Doup\u00e9, A., Ahn, G.-J.: Behind closed doors: measurement and analysis of cryptolocker ransoms in bitcoin. In: 2016 APWG Symposium on Electronic Crime Research (eCrime), pp. 1\u201313. IEEE (2016)","DOI":"10.1109\/ECRIME.2016.7487938"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Paquet-Clouston, M., Haslhofer, B., Dupont, B.: Ransomware payments in the bitcoin ecosystem. J. Cybersecur. 5(1), tyz003 (2019)","DOI":"10.1093\/cybsec\/tyz003"},{"key":"8_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-642-39884-1_4","volume-title":"Financial Cryptography and Data Security","author":"E Androulaki","year":"2013","unstructured":"Androulaki, E., Karame, G.O., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 34\u201351. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39884-1_4"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Toyoda, K., Ohtsuki, T., Takis Mathiopoulos, P.: Multi-class bitcoin-enabled service identification based on transaction history summarization. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1153\u20131160. IEEE (2018)","DOI":"10.1109\/Cybermatics_2018.2018.00208"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Jourdan, M., Blandin, S., Wynter, L., Deshpande, P.: Characterizing entities in the bitcoin blockchain. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 55\u201362. IEEE (2018)","DOI":"10.1109\/ICDMW.2018.00016"},{"issue":"1","key":"8_CR21","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"4","key":"8_CR22","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18\u201328 (1998)","journal-title":"IEEE Intell. Syst. Appl."},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"8_CR24","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/2.485891","volume":"29","author":"AK Jain","year":"1996","unstructured":"Jain, A.K., Mao, J., Moidin Mohiuddin, K.: Artificial neural networks: a tutorial. Computer 29(3), 31\u201344 (1996)","journal-title":"Computer"},{"key":"8_CR25","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR26","unstructured":"Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd. (2017)"},{"key":"8_CR27","unstructured":"Longadge, R., Dongre, S.: Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707 (2013)"},{"key":"8_CR28","unstructured":"Nguyen, G.H., Bouzerdoum, A., Phung, S.L.: Learning pattern classification tasks with imbalanced data sets. In Pattern recognition, IntechOpen (2009)"},{"issue":"12","key":"8_CR29","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun, Y., et al.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358\u20133378 (2007)","journal-title":"Pattern Recogn."},{"key":"8_CR30","unstructured":"Bahnsen, A.C.: Ensembles of example-dependent cost-sensitive decision trees (2015)"},{"issue":"2","key":"8_CR31","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1002\/aic.690370209","volume":"37","author":"MA Kramer","year":"1991","unstructured":"Kramer, M.A.: Nonlinear principal component analysis using auto associative neural networks. AIChE J. 37(2), 233\u2013243 (1991)","journal-title":"AIChE J."},{"issue":"8","key":"8_CR32","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."}],"container-title":["Communications in Computer and Information Science","Blockchain and Trustworthy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-9213-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T00:17:16Z","timestamp":1619309836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-15-9213-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811592126","9789811592133"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-9213-3_8","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BlockSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Blockchain and Trustworthy Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"blocksys2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/blocksys.info\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"100","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}