{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T02:43:01Z","timestamp":1768876981224,"version":"3.49.0"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030712136","type":"print"},{"value":"9783030712143","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-71214-3_17","type":"book-chapter","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T11:04:17Z","timestamp":1616583857000},"page":"205-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Bitcoin Abnormal Transaction Detection Based on Machine Learning"],"prefix":"10.1007","author":[{"given":"Elena V.","family":"Feldman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5996-669X","authenticated-orcid":false,"given":"Alexey N.","family":"Ruchay","sequence":"additional","affiliation":[]},{"given":"Veronica K.","family":"Matveeva","sequence":"additional","affiliation":[]},{"given":"Valeria D.","family":"Samsonova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"17_CR1","unstructured":"Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics (2019)"},{"key":"17_CR2","unstructured":"Elliptic data set. https:\/\/www.kaggle.com\/ellipticco\/elliptic-data-set"},{"key":"17_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-030-10549-5_26","volume-title":"Euro-Par 2018: Parallel Processing Workshops","author":"S Bistarelli","year":"2019","unstructured":"Bistarelli, S., Mercanti, I., Santini, F.: A suite of tools for the forensic analysis of bitcoin transactions: preliminary report. In: Mencagli, G., et al. (eds.) Euro-Par 2018. LNCS, vol. 11339, pp. 329\u2013341. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10549-5_26"},{"key":"17_CR4","first-page":"768","volume":"8","author":"K Kedharewsari","year":"2016","unstructured":"Kedharewsari, K., Maria Anu, M., Rajalakshmi, V.: Integration of big data & cloud computing to detect black money rotation with range - aggregate queries. Int. J. Eng. Technol. 8, 768\u2013773 (2016)","journal-title":"Int. J. Eng. Technol."},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Maksutov, A.A., Alexeev, M.S., Fedorova, N.O., Andreev, D.A.: Detection of blockchain transactions used in blockchain mixer of coin join type. In: 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 274\u2013277 (2019)","DOI":"10.1109\/EIConRus.2019.8656687"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Oakley, J., Worley, C., Yu, L., Brooks, R., Skjellum, A.: Unmasking criminal enterprises: an analysis of bitcoin transactions. In: 2018 13th International Conference on Malicious and Unwanted Software (MALWARE), pp. 161\u2013166 (2018)","DOI":"10.1109\/MALWARE.2018.8659357"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Plaksiy, K., Nikiforov, A., Miloslavskaya, N.: Applying big data technologies to detect cases of money laundering and counter financing of terrorism. In: 2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pp. 70\u201377 (2018)","DOI":"10.1109\/W-FiCloud.2018.00017"},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/07421222.2018.1550550","volume":"36","author":"H Yin","year":"2019","unstructured":"Yin, H., Langenheldt, K., Harlev, M., Mukkamala, R.R., Vatrapu, R.: Regulating cryptocurrencies: a supervised machine learning approach to de-anonymizing the bitcoin blockchain. J. Manage. Inf. Syst. 36, 37\u201373 (2019)","journal-title":"J. Manage. Inf. Syst."},{"key":"17_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1706-8","volume-title":"Machine Learning Approaches in Cyber Security Analytics","author":"T Thomas","year":"2020","unstructured":"Thomas, T., P. Vijayaraghavan, A., Emmanuel, S.: Machine Learning Approaches in Cyber Security Analytics. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-1706-8"},{"key":"17_CR10","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)"},{"key":"17_CR11","volume-title":"Information Theory. Inference & Learning Algorithms","author":"DJC MacKay","year":"2003","unstructured":"MacKay, D.J.C.: Information Theory. Inference & Learning Algorithms. Cambridge University Press, USA (2003)"},{"key":"17_CR12","first-page":"769","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. 6, 769\u2013772 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"17_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/978-3-642-02172-5_57","volume-title":"Pattern Recognition and Image Analysis","author":"V Garc\u00eda","year":"2009","unstructured":"Garc\u00eda, V., Mollineda, R.A., S\u00e1nchez, J.S.: Index of balanced accuracy: a performance measure for skewed class distributions. In: Araujo, H., Mendon\u00e7a, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. LNCS, vol. 5524, pp. 441\u2013448. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-02172-5_57"},{"key":"17_CR14","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":"17_CR15","unstructured":"Dorogush, A.V., Ershov, V., Gulin, A.: CatBoost: gradient boosting with categorical features support. CoRR abs\/1810.11363 (2018)"},{"key":"17_CR16","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. KDD 2016, New York, NY, USA, Association for Computing Machinery, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"17_CR17","unstructured":"Ke, G., et al.: LightGBm: a highly efficient gradient boosting decision tree. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 3146\u20133154. Curran Associates, Inc. (2017)"},{"key":"17_CR18","first-page":"559","volume":"18","author":"G Lema\u00eetre","year":"2016","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18, 559\u2013563 (2016)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Communications in Computer and Information Science","Recent Trends in Analysis of Images, Social Networks and Texts"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71214-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T00:51:16Z","timestamp":1619225476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71214-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030712136","9783030712143"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71214-3_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"25 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Analysis of Images, Social Networks and Texts","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Moscow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","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":"15 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aist2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aistconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"108","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":"41","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":"13","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":"38% - 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":"3.1","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":"2.33","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online","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)"}}]}}