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Acknowledging the challenge of limited intelligence in the early stages of new chains, we propose ADA-Spear-an automatic phishing detection model utilizing <jats:italic>a<\/jats:italic>dversarial <jats:italic>d<\/jats:italic>omain <jats:italic>a<\/jats:italic>daptive learning which symbolizes the method\u2019s ability to penetrate various heterogeneous blockchains for phishing detection. The model effectively identifies phishing behavior in new chains with limited reliable labels, addressing challenges such as significant distribution drift, low attribute overlap, and limited inter-chain connections. Our approach includes a subgraph construction strategy to align heterogeneous chains, a layered deep learning encoder capturing both temporal and spatial information, and integrated adversarial domain adaptive learning in end-to-end model training. Validation in Ethereum, Bitcoin, and EOSIO environments demonstrates ADA-Spear\u2019s effectiveness, achieving an average F1 score of 77.41 on new chains after knowledge transfer, surpassing existing detection methods.<\/jats:p>","DOI":"10.1186\/s42400-024-00237-5","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T02:01:42Z","timestamp":1718762502000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Phishing behavior detection on different blockchains via adversarial domain adaptation"],"prefix":"10.1186","volume":"7","author":[{"given":"Chuyi","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueying","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhigang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuling","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"237_CR1","doi-asserted-by":"crossref","unstructured":"Aggarwal CC et al (2015) Data mining: the textbook, vol 1","DOI":"10.1007\/978-3-319-14142-8_1"},{"key":"237_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11280-021-00875-6","volume":"24","author":"X Ao","year":"2021","unstructured":"Ao X, Liu Y, Qin Z, Sun Y, He Q (2021) Temporal high-order proximity aware behavior analysis on Ethereum. 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