Semi‐selfish mining based on hidden Markov decision process

International Journal of Intelligent SystemsVolume 36, Issue 7 p. 3596-3612 RESEARCH ARTICLE Semi-selfish mining based on hidden Markov decision process Tao Li, orcid.org/0000-0002-1448-3619 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, ChinaSearch for more papers by this authorZhaojie Wang, orcid.org/0000-0002-9483-8204 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorGuoyu Yang, orcid.org/0000-0002-9777-6790 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYang Cui, orcid.org/0000-0002-8892-515X School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYuling Chen, Corresponding Author ylchen3@gzu.edu.cn orcid.org/0000-0002-8674-8356 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China Correspondence Yuling Chen, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China. Email: ylchen3@gzu.edu.cnSearch for more papers by this authorXiaomei Yu, orcid.org/0000-0002-8342-6714 School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this author Tao Li, orcid.org/0000-0002-1448-3619 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, ChinaSearch for more papers by this authorZhaojie Wang, orcid.org/0000-0002-9483-8204 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorGuoyu Yang, orcid.org/0000-0002-9777-6790 School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYang Cui, orcid.org/0000-0002-8892-515X School of Computer Science, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYuling Chen, Corresponding Author ylchen3@gzu.edu.cn orcid.org/0000-0002-8674-8356 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China Correspondence Yuling Chen, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China. Email: ylchen3@gzu.edu.cnSearch for more papers by this authorXiaomei Yu, orcid.org/0000-0002-8342-6714 School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this author First published: 06 April 2021 https://doi.org/10.1002/int.22428Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi-selfish miners), who mine on the private chain, to mine on public chain with a small probability ρ. Simulation results show that SMHMDP can trade off between revenues and forking rate. Put differently, selfish miners benefit from attacking within an acceptable forking rate toward the view of honest parties, without leading selfish mining attacks to be an armchair strategist. Citing Literature Volume36, Issue7July 2021Pages 3596-3612 RelatedInformation