{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:12:12Z","timestamp":1767705132880,"version":"3.44.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s41060-024-00703-w","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:10:37Z","timestamp":1734945037000},"page":"3951-3964","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring machine learning-based methods for anomalies detection: evidence from cryptocurrencies"],"prefix":"10.1007","volume":"20","author":[{"given":"Achraf","family":"Yahia","sequence":"first","affiliation":[]},{"given":"Yassine","family":"Mouhssine","sequence":"additional","affiliation":[]},{"given":"Abdelkader","family":"El Alaoui","sequence":"additional","affiliation":[]},{"given":"Said Ouatik","family":"El Alaoui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"703_CR1","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1080\/08874417.2018.1538709","volume":"60","author":"E Zamani","year":"2020","unstructured":"Zamani, E., He, Y., Phillips, M.: On the security risks of the blockchain. J. Comput. Inf. Syst. 60, 495\u2013506 (2020). https:\/\/doi.org\/10.1080\/08874417.2018.1538709","journal-title":"J. Comput. Inf. Syst."},{"key":"703_CR2","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1080\/08874417.2018.1552090","volume":"60","author":"A Vo","year":"2020","unstructured":"Vo, A., Yost-Bremm, C.: A high-frequency algorithmic trading strategy for cryptocurrency. J. Comput. Inf. Syst. 60, 555\u2013568 (2020). https:\/\/doi.org\/10.1080\/08874417.2018.1552090","journal-title":"J. Comput. Inf. Syst."},{"unstructured":"Poyser, O.: Herding behavior in cryptocurrency markets, (2018) http:\/\/arxiv.org\/abs\/1806.11348","key":"703_CR3"},{"key":"703_CR4","doi-asserted-by":"publisher","DOI":"10.1080\/15427560.2022.2073593","author":"A Nepp","year":"2022","unstructured":"Nepp, A., Karpeko, F.: Hype as a factor on the global market: the case of Bitcoin. J. Behav. Finance (2022). https:\/\/doi.org\/10.1080\/15427560.2022.2073593","journal-title":"J. Behav. Finance"},{"key":"703_CR5","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1080\/08874417.2018.1477076","volume":"60","author":"A Zimba","year":"2020","unstructured":"Zimba, A., Wang, Z., Mulenga, M., Odongo, N.H.: Crypto mining attacks in information systems: an emerging threat to cyber security. J. Comput. Inf. Syst. 60, 297\u2013308 (2020). https:\/\/doi.org\/10.1080\/08874417.2018.1477076","journal-title":"J. Comput. Inf. Syst."},{"key":"703_CR6","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.jfds.2021.03.001","volume":"7","author":"P Jaquart","year":"2021","unstructured":"Jaquart, P., Dann, D., Weinhardt, C.: Short-term bitcoin market prediction via machine learning. J. Finance Data Sci. 7, 45\u201366 (2021). https:\/\/doi.org\/10.1016\/j.jfds.2021.03.001","journal-title":"J. Finance Data Sci."},{"key":"703_CR7","doi-asserted-by":"publisher","first-page":"10127","DOI":"10.1109\/ACCESS.2018.2890507","volume":"7","author":"K Salah","year":"2019","unstructured":"Salah, K., Rehman, M.H.U., Nizamuddin, N., Al-Fuqaha, A.: Blockchain for AI: Review and Open Research Challenges. IEEE Access. 7, 10127\u201310149 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2018.2890507","journal-title":"IEEE Access."},{"key":"703_CR8","doi-asserted-by":"publisher","first-page":"127799","DOI":"10.1016\/j.physa.2022.127799","volume":"604","author":"Z Gu","year":"2022","unstructured":"Gu, Z., Lin, D., Wu, J.: On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges. Phys. A Stat. Mech. Appl. 604, 127799 (2022). https:\/\/doi.org\/10.1016\/j.physa.2022.127799","journal-title":"Phys. A Stat. Mech. Appl."},{"issue":"3","key":"703_CR9","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1007\/s10586-021-03511-0","volume":"25","author":"B K\u0131l\u0131\u00e7","year":"2021","unstructured":"K\u0131l\u0131\u00e7, B., Ozturan, C., San, A.: Parallel analysis of Ethereum blockchain transaction data using cluster computing. Cluster Comput. 25(3), 1885\u20131898 (2021). https:\/\/doi.org\/10.1007\/s10586-021-03511-0","journal-title":"Cluster Comput."},{"key":"703_CR10","doi-asserted-by":"publisher","first-page":"147","DOI":"10.3390\/s20010147","volume":"20","author":"B Podgorelec","year":"2019","unstructured":"Podgorelec, B., Turkanovi\u0107, M., Karakati\u010d, S.: A Machine learning-based method for automated blockchain transaction signing including personalized anomaly detection. Sensors. 20, 147 (2019). https:\/\/doi.org\/10.3390\/s20010147","journal-title":"Sensors."},{"unstructured":"Hassan, M.U., Rehmani, M.H., Chen, J.: Anomaly detection in blockchain networks: a comprehensive survey, (2022) http:\/\/arxiv.org\/abs\/2112.06089","key":"703_CR11"},{"key":"703_CR12","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ins.2019.04.013","volume":"492","author":"D Guo","year":"2019","unstructured":"Guo, D., Dong, J., Wang, K.: Graph structure and statistical properties of Ethereum transaction relationships. Inf. Sci. 492, 58\u201371 (2019). https:\/\/doi.org\/10.1016\/j.ins.2019.04.013","journal-title":"Inf. Sci."},{"key":"703_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115480","volume":"184","author":"H-M Kim","year":"2021","unstructured":"Kim, H.-M., Bock, G.-W., Lee, G.: Predicting ethereum prices with machine learning based on blockchain information. Expert Syst. Appl. 184, 115480 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115480","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"crossref","unstructured":"Cai, Y.: How is price explosivity triggered in the cryptocurrency markets? Ann. Oper. Res. (2021)","key":"703_CR14","DOI":"10.1007\/s10479-021-04298-4"},{"key":"703_CR15","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10479-020-03680-y","volume":"313","author":"TLD Huynh","year":"2022","unstructured":"Huynh, T.L.D., Shahbaz, M., Nasir, M.A., Ullah, S.: Financial modelling, risk management of energy instruments and the role of cryptocurrencies. Ann. Oper. Res. 313, 47\u201375 (2022). https:\/\/doi.org\/10.1007\/s10479-020-03680-y","journal-title":"Ann. Oper. Res."},{"key":"703_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2020.104869","volume":"134","author":"S Russo","year":"2020","unstructured":"Russo, S., L\u00fcrig, M., Hao, W., Matthews, B., Villez, K.: Active learning for anomaly detection in environmental data. Environ Model Softw. 134, 104869 (2020)","journal-title":"Environ Model Softw."},{"key":"703_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1007\/978-3-030-65745-1_8","volume-title":"Network and System Security","author":"V Patel","year":"2020","unstructured":"Patel, V., Pan, L., Rajasegarar, S.: Graph deep learning based anomaly detection in ethereum blockchain network. In: Kuty\u0142owski, M., Zhang, J., Chen, C. (eds.) Network and System Security, pp. 132\u2013148. Springer International Publishing, Cham (2020)"},{"key":"703_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0218341","volume":"14","author":"F-B Shi","year":"2019","unstructured":"Shi, F.-B., Sun, X.-Q., Gao, J.-H., Xu, L., Shen, H.-W., Cheng, X.-Q.: Anomaly detection in Bitcoin market via price return analysis. PLoS ONE 14, e0218341 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0218341","journal-title":"PLoS ONE"},{"unstructured":"Pham, T., Lee, S.: Anomaly detection in the bitcoin system - a network perspective, (2017) http:\/\/arxiv.org\/abs\/1611.03942","key":"703_CR19"},{"unstructured":"Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods, (2017) http:\/\/arxiv.org\/abs\/1611.03941","key":"703_CR20"},{"key":"703_CR21","doi-asserted-by":"publisher","first-page":"619","DOI":"10.52783\/jes.2988","volume":"20","author":"S Siddamsetti","year":"2024","unstructured":"Siddamsetti, S., Tejaswi, C., Maddula, P.: Anomaly detection in blockchain using machine learning. J. Electr. Syst. 20, 619\u2013634 (2024)","journal-title":"J. Electr. Syst."},{"key":"703_CR22","doi-asserted-by":"publisher","first-page":"201","DOI":"10.3390\/a17050201","volume":"17","author":"C Cholevas","year":"2024","unstructured":"Cholevas, C., Angeli, E., Sereti, Z., Mavrikos, E., Tsekouras, G.E.: Anomaly detection in blockchain networks using unsupervised learning: a survey. Algorithms 17, 201 (2024). https:\/\/doi.org\/10.3390\/a17050201","journal-title":"Algorithms"},{"key":"703_CR23","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.patrec.2021.05.022","volume":"149","author":"J Lesouple","year":"2021","unstructured":"Lesouple, J., Baudoin, C., Spigai, M., Tourneret, J.-Y.: Generalized isolation forest for anomaly detection. Pattern Recogn. Lett. 149, 109\u2013119 (2021). https:\/\/doi.org\/10.1016\/j.patrec.2021.05.022","journal-title":"Pattern Recogn. Lett."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00703-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-024-00703-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00703-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:53:24Z","timestamp":1758797604000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-024-00703-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":23,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["703"],"URL":"https:\/\/doi.org\/10.1007\/s41060-024-00703-w","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"16 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}