{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T11:23:19Z","timestamp":1772623399185,"version":"3.50.1"},"reference-count":27,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Ever since Ether is launched as a digital currency, its rise has been rapid. It is currently the second most valuable digital currency in the world. There are more than 1 million transactions happening on the Ethereum network every day, and this number is expected to continue to increase. Due to the increasing number of transactions, fraudulent transactions have also increased, which has resulted in a large amount of money being lost and has also destroyed the livelihoods of many individuals. Due to their similarity to valid transactions, it is extremely difficult to distinguish between them. Additionally, Ethereum's pseudo\u2010anonymity adds to the difficulty of identifying the parties involved. Since there are millions of transactions every day, it would be difficult to manually verify each one. Therefore, a mechanism for validating these transactions is needed. In this context, this paper proposes a novel approach to detecting fraudulent accounts associated with these transactions by implementing machine learning algorithms among the given set of transactions. We propose a framework for creating a stacking classifier by combining several standalone classification algorithms and creating a meta\u2010learner based on the output of each base algorithm. The algorithms include Logistic Regression, Naive Bayes, Decision Trees, Random Forests, AdaBoosts, KNNs, SVMs, and Gradient Boosts. As a result of combining these algorithms, a powerful classifier with the ability to detect fraudulent transactions. A variety of machine learning models were trained and evaluated on the test set using various metrics. Based on the results of the individual algorithm the Random Forest algorithm achieved the highest accuracy of 95.47%, followed by Gradient Boosting at 94.61% which is an ensemble algorithm using the boosting technique. The Stacking classifier that combines Multinomial Naive Bayes and Random Forest as the base learners and logistic regression as the Meta learner achieved the highest accuracy of 97.18% with an F1 score of 97.02%. Based on the results of all the stacking models developed, it is concluded that algorithms tend to perform better when combined properly. When compared to the other approaches, the proposed approach has outperformed the others, making it feasible in the real world to detect fraudulent transactions.<\/jats:p>","DOI":"10.1111\/exsy.13255","type":"journal-article","created":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T18:00:53Z","timestamp":1676743253000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A novel approach to detect fraud in Ethereum transactions using stacking"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0569-9960","authenticated-orcid":false,"given":"Abdul Quadir","family":"Md","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering Vellore Institute of Technology  Chennai India"}]},{"given":"S. M. Satya Sree","family":"Narayanan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Vellore Institute of Technology  Chennai India"}]},{"given":"H.","family":"Sabireen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Vellore Institute of Technology  Chennai India"}]},{"given":"Arun Kumar","family":"Sivaraman","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Vellore Institute of Technology  Chennai India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-873X","authenticated-orcid":false,"given":"Kong Fah","family":"Tee","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Quantity Surveying INTI International University  Nilai Malaysia"}]}],"member":"311","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22197162"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","unstructured":"Baek H. Oh J. Kim C. Y. &Lee K.(2019).A model for detecting cryptocurrency transactions with discernible purpose. 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN) pp. 713\u2013717.https:\/\/doi.org\/10.1109\/ICUFN.2019.8806126","DOI":"10.1109\/ICUFN.2019.8806126"},{"key":"e_1_2_8_4_1","doi-asserted-by":"crossref","unstructured":"Bhowmik M. Chandana T. S. S. &Rudra B.(2021).Comparative study of machine learning algorithms for fraud detection in blockchain. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) pp. 539\u2013541.https:\/\/doi.org\/10.1109\/ICCMC51019.2021.9418470","DOI":"10.1109\/ICCMC51019.2021.9418470"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"Chen W. Li X. Sui Y. He N. Wang H. Wu L. &Luo X.(2021).SADPonzi: Detecting and characterizing Ponzi schemes in Ethereum smart contracts. Proc. ACM Meas. Anal. Comput. Syst. 5 2 Article 26 (June 2021) 30.https:\/\/doi.org\/10.1145\/3460093","DOI":"10.1145\/3460093"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"Chen W. Zheng Z. Cui J. Ngai E. Zheng P. &Zhou Y.(2018).Detecting Ponzi schemes on Ethereum: Towards healthier blockchain technology. In Proceedings of the 2018 World Wide Web Conference (WWW'18). International World Wide Web Conferences Steering Committee Republic and Canton of Geneva CHE pp. 1409\u20131418. doi:10.1145\/3178876.3186046","DOI":"10.1145\/3178876.3186046"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3152546"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113318"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"Ibba G. Pierro G. A. &Di Francesco M.(2021).Evaluating machine\u2010learning techniques for detecting smart Ponzi schemes. 2021 IEEE\/ACM 4th international workshop on emerging trends in software engineering for blockchain (WETSEB) pp. 34\u201340.https:\/\/doi.org\/10.1109\/WETSEB52558.2021.00012","DOI":"10.1109\/WETSEB52558.2021.00012"},{"key":"e_1_2_8_10_1","doi-asserted-by":"crossref","unstructured":"Ibrahim R. F. 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B. &Zilic Z.(2020).Detecting malicious Ethereum entities via application of machine learning classification. 2020 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS) pp. 120\u2013127.https:\/\/doi.org\/10.1109\/BRAINS49436.2020.9223304","DOI":"10.1109\/BRAINS49436.2020.9223304"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-189173"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102462"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"Xuan S. Liu G. Li Z. Zheng L. 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Yan S. &Zhang J.(2022).Prediction and analysis of illegal accounts on Ethereum based on Catboost algorithm. 2022 international conference on big data information and computer network (BDICN) pp. 63\u201367.https:\/\/doi.org\/10.1109\/BDICN55575.2022.00020","DOI":"10.1109\/BDICN55575.2022.00020"}],"container-title":["Expert Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.13255","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/exsy.13255","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.13255","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T02:44:20Z","timestamp":1692326660000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/exsy.13255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":27,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.1111\/exsy.13255"],"URL":"https:\/\/doi.org\/10.1111\/exsy.13255","archive":["Portico"],"relation":{},"ISSN":["0266-4720","1468-0394"],"issn-type":[{"value":"0266-4720","type":"print"},{"value":"1468-0394","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,17]]},"assertion":[{"value":"2022-07-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e13255"}}