IPBSM: An optimal bribery selfish mining in the presence of intelligent and pure attackers

International Journal of Intelligent SystemsVolume 35, Issue 11 p. 1735-1748 RESEARCH ARTICLE IPBSM: An optimal bribery selfish mining in the presence of intelligent and pure attackers Guoyu Yang, Guoyu Yang School of Information Science and Engineering, Qufu Normal University, Rizhao, China Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorYilei Wang, Corresponding Author Yilei Wang ylwangqfnu@163.com School of Information Science and Engineering, Qufu Normal University, Rizhao, China Correspondence Yilei Wang, School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China. Email: ylwangqfnu@163.comSearch for more papers by this authorZhaojie Wang, Zhaojie Wang School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYouliang Tian, Youliang Tian State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guizhou, ChinaSearch for more papers by this authorXiaomei Yu, Xiaomei Yu School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this authorShouzhe Li, Shouzhe Li Department of Computer Science, University of Wisconsin-Madison, Wisconsin, Madison, USASearch for more papers by this author Guoyu Yang, Guoyu Yang School of Information Science and Engineering, Qufu Normal University, Rizhao, China Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorYilei Wang, Corresponding Author Yilei Wang ylwangqfnu@163.com School of Information Science and Engineering, Qufu Normal University, Rizhao, China Correspondence Yilei Wang, School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China. Email: ylwangqfnu@163.comSearch for more papers by this authorZhaojie Wang, Zhaojie Wang School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorYouliang Tian, Youliang Tian State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guizhou, ChinaSearch for more papers by this authorXiaomei Yu, Xiaomei Yu School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this authorShouzhe Li, Shouzhe Li Department of Computer Science, University of Wisconsin-Madison, Wisconsin, Madison, USASearch for more papers by this author First published: 06 August 2020 https://doi.org/10.1002/int.22270Citations: 7Read 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 onFacebookTwitterLinkedInRedditWechat Abstract Blockchain is a "decentralized" system, where the security heavily depends on that of the consensus protocols. For instance, attackers gain illegal revenues by leveraging the vulnerabilities of the consensus protocols. Such attacks consist of selfish mining (SM1), optimal selfish mining ( ϵ-optimal), bribery selfish mining (BSM), and so forth. In existing works, the attacks only consider the circumstances, where part of miners are rational. However, miners are hardly nonrational in the blockchain system since they hope to maximize their revenues. Furthermore, attackers prefer intelligent tools to increase their power for more additional revenues. Therefore, new models are urgently needed to formulate the scenarios, where attackers are purely rational and intelligent. In this paper, we propose a new BSM model, where all miners are rational. Moreover, rational attackers are intelligent such that they optimize their strategies by utilizing reinforcement learning to boost their revenues. More specifically, we propose a new selfish mining algorithm: intelligent bribery selfish mining (IPBSM), where attackers choose optimal strategies resorting to reinforcement learning when they interact with the external environment. The external environment can be further modeled as a Markov decision process to facilitate the construction of reinforcement learning. The simulation results manifest that IPBSM, compared with SM1 and ϵ-optimal, has lower power thresholds and higher revenues. Therefore, IPBSM is a threat no to be neglected to the blockchain system. Citing Literature Volume35, Issue11November 2020Pages 1735-1748 RelatedInformation