{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:10:08Z","timestamp":1768205408424,"version":"3.49.0"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100010807","name":"Firat University Scientific Research Projects Management Unit","doi-asserted-by":"publisher","award":["ADEP.25.28"],"award-info":[{"award-number":["ADEP.25.28"]}],"id":[{"id":"10.13039\/501100010807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The proliferation of phishing scam tokens on the Ethereum blockchain, including honeypot, rug pull, and impersonation schemes, poses a grave threat to financial security. Although earlier studies have documented detection accuracies that exceed 95%, they frequently depend on random train\u2010test partitions. These partitions frequently overestimate real\u2010world performance by disregarding the temporal progression of phishing behaviors. This study addresses the methodological gap by employing a temporally validated evaluation. A labeled dataset comprising 5408 Ethereum token contracts was constructed. This dataset was verified through a two\u2010stage process that integrated cyber threat intelligence and on\u2010chain evidence. A total of 16 discriminative features were extracted, reflecting transaction volume, network structure, and temporal behavior. In lieu of employing random partitioning, temporal validation (70% training, 15% validation, and 15% testing) was adopted to assess generalizability to emerging threats. Six machine learning models (LightGBM, XGBoost, Random Forest, Gradient Boosting, Decision Tree, and MLP) were tuned via GridSearchCV. LightGBM demonstrated optimal performance, attaining 85.59% accuracy, 81.63% F1\u2010score, and 92.02% AUC on temporally held\u2010out data. The feature ablation process yielded the identification of transaction volume as the most discriminative factor, with a corresponding increase in performance of 13.09 points on the performance scale. Conversely, temporal features exhibited a marginal decline in performance, with a decrease of 0.87 points. Temporal validation resulted in a 3.95\u2010point\u2010percentage decrease compared to random splitting, thereby exposing the optimistic bias present in prior studies. Despite the fact that the resulting F1\u2010score of 81.63% falls short of the 85% threshold stipulated in the literature, it is indicative of a realistic deployment expectation. This work underscores the importance of temporal validation for reliable fraud detection research.<\/jats:p>","DOI":"10.1002\/cpe.70503","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T13:50:19Z","timestamp":1765288219000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Feature Extraction and Detection Model for Phishing Scam on Ethereum Using Machine Learning"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9736-8068","authenticated-orcid":false,"given":"Fatih","family":"Ertam","sequence":"first","affiliation":[{"name":"Department of Digital Forensics Engineering Firat University  Elazig T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duzgun","family":"Kucuk","sequence":"additional","affiliation":[{"name":"Brandefense Inc.  Ankara T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilhan Firat","family":"Kilincer","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering Firat University  Elazig T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-019-0147-z"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2024.103858"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2020.100125"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2025.103074"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2022.03.007"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.137115"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7724"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7706"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/spy2.96"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128227"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2025.111123"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","unstructured":"S.Li G.Gou C.Liu C.Hou Z.Li andG.Xiong \u201cTTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection WWW 2022\u2014Proceedings of the ACM on Web Conference 2022 \u201d(2022).","DOI":"10.1145\/3485447.3512226"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124941"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103412"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111698"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103561"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.kscej.2025.100281"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2022.109064"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"issue":"37","key":"e_1_2_11_21_1","article-title":"A Next\u2010Generation Smart Contract and Decentralized Application Platform","volume":"3","author":"Buterin V.","year":"2014","journal-title":"White paper"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/spy2.260"},{"key":"e_1_2_11_23_1","first-page":"1","article-title":"Ethereum: A Secure Decentralised Generalised Transaction Ledger","volume":"151","author":"Wood G.","year":"2014","journal-title":"Ethereum Project Yellow Paper"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2023.103732"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103384"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113318"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118463"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103479"},{"key":"e_1_2_11_29_1","doi-asserted-by":"crossref","unstructured":"Q.Yuan B.Huang J.Zhang J.Wu H.Zhang andX.Zhang \u201cDetecting Phishing Scams on Ethereum 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