{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T00:21:15Z","timestamp":1759450875857,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U21A20463"],"award-info":[{"award-number":["U21A20463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Program of Guangzhou","award":["2024A03J0403"],"award-info":[{"award-number":["2024A03J0403"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Symmetry"],"abstract":"<jats:p>Proof-of-Work (PoW) blockchains with symmetric consensus face threats such as selfish mining, bribery mining, block withholding, and replay attacks. This paper introduces a hybrid attack, Bribery\u2013Stubborn Mining (BSbM), which integrates stubborn mining\u2019s delayed chain publication with bribery incentives to recruit miners during forks. Simulation experiments confirm that BSbM yields additional revenue. To obtain even higher revenue, we propose Leading Hidden Bribery\u2013Stubborn Mining (LHBSbM) based on BSbM. By concealing and delaying broadcasts, LHBSbM constructs a triple fork, maintaining three chains at the same height. Upon revealing the private chain, two public blocks can be isolated, breaking the single-block limit of double-fork attacks. Theoretical analysis shows that LHBSbM raises the attacker\u2019s maximum effective block rate from \u03b1\/(1\u2212\u03b1) to \u03b1\/(1\u2212\u03b1\u2212\u03b2). Experimental results indicate that, under ideal conditions (r=0), BSbM becomes profitable once the attacker\u2019s hash rate (\u03b1) exceeds approximately 34% and further confirm that, under certain conditions, LHBSbM nearly doubles isolated blocks compared to BSbM, yielding greater profits. Finally, potential defenses against such hybrid attacks are discussed, offering new insights for blockchain security.<\/jats:p>","DOI":"10.3390\/sym17101618","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T08:21:51Z","timestamp":1759220511000},"page":"1618","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Bribery\u2013Stubborn Mining to Leading Hidden Triple-Fork Strategies for Incentive Optimization in PoW Blockchains"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1598-9218","authenticated-orcid":false,"given":"Weijie","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Blockchain Security, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bina","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Blockchain Security, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weipeng","family":"Liang","sequence":"additional","affiliation":[{"name":"China Telecom Corporation Limited Jiangmen Branch, Jiangmen 529000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-2293","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guangzhou University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Blockchain Security, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"ref_1","unstructured":"Nakamoto, S. 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