Optimal mixed block withholding attacks based on reinforcement learning

International Journal of Intelligent SystemsVolume 35, Issue 12 p. 2032-2048 RESEARCH ARTICLE Optimal mixed block withholding attacks based on reinforcement learning Yilei Wang, Corresponding Author ylwangqfnu@163.com orcid.org/0000-0003-4054-0549 School of Information Science and Engineering, Qufu Normal University, Rizhao, China Correspondence Yilei Wang, School of Information Science and Engineering, Qufu Normal University, Rizhao 276825, China. Email: ylwangqfnu@163.comSearch for more papers by this authorGuoyu Yang, orcid.org/0000-0002-9777-6790 School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorTao Li, orcid.org/0000-0002-1874-1910 School of Information Science and Engineering, Qufu Normal University, Rizhao, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China College of Computer Science and Technology, Guizhou University, Guiyang, ChinaSearch for more papers by this authorLifeng Zhang, orcid.org/0000-0003-1833-6760 School of Information and Electrical Engineering, Ludong University, Yantai, ChinaSearch for more papers by this authorYanli Wang, orcid.org/0000-0003-0575-172X School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorLishan Ke, orcid.org/0000-0002-3892-8598 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorYi Dou, orcid.org/0000-0003-4995-4502 Department of Computing, Hong Kong Polytechnic University, Hong Kong, ChinaSearch for more papers by this authorShouzhe Li, orcid.org/0000-0003-3597-3403 Department of Computer Science, University of Wisconsin-Madison, Madison, Wisconsin, USASearch for more papers by this authorXiaomei Yu, orcid.org/0000-0003-1543-2714 School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this author Yilei Wang, Corresponding Author ylwangqfnu@163.com orcid.org/0000-0003-4054-0549 School of Information Science and Engineering, Qufu Normal University, Rizhao, China Correspondence Yilei Wang, School of Information Science and Engineering, Qufu Normal University, Rizhao 276825, China. Email: ylwangqfnu@163.comSearch for more papers by this authorGuoyu Yang, orcid.org/0000-0002-9777-6790 School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorTao Li, orcid.org/0000-0002-1874-1910 School of Information Science and Engineering, Qufu Normal University, Rizhao, China State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China College of Computer Science and Technology, Guizhou University, Guiyang, ChinaSearch for more papers by this authorLifeng Zhang, orcid.org/0000-0003-1833-6760 School of Information and Electrical Engineering, Ludong University, Yantai, ChinaSearch for more papers by this authorYanli Wang, orcid.org/0000-0003-0575-172X School of Information Science and Engineering, Qufu Normal University, Rizhao, ChinaSearch for more papers by this authorLishan Ke, orcid.org/0000-0002-3892-8598 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, ChinaSearch for more papers by this authorYi Dou, orcid.org/0000-0003-4995-4502 Department of Computing, Hong Kong Polytechnic University, Hong Kong, ChinaSearch for more papers by this authorShouzhe Li, orcid.org/0000-0003-3597-3403 Department of Computer Science, University of Wisconsin-Madison, Madison, Wisconsin, USASearch for more papers by this authorXiaomei Yu, orcid.org/0000-0003-1543-2714 School of Information Science and Engineering, Shandong Normal University, Jinan, ChinaSearch for more papers by this author First published: 01 September 2020 https://doi.org/10.1002/int.22282Citations: 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 The vulnerabilities in cryptographic currencies facilitate the adversarial attacks. Therefore, the attackers have incentives to increase their rewards by strategic behaviors. Block withholding attacks (BWH) are such behaviors that attackers withhold blocks in the target pools to subvert the blockchain ecosystem. Furthermore, BWH attacks may dwarf the countermeasures by combining with selfish mining attacks or other strategic behaviors, for example, fork after withholding (FAW) attacks and power adaptive withholding (PAW) attacks. That is, the attackers may be intelligent enough such that they can dynamically gear their behaviors to optimal attacking strategies. In this paper, we propose mixed-BWH attacks with respect to intelligent attackers, who leverage reinforcement learning to pin down optimal strategic behaviors to maximize their rewards. More specifically, the intelligent attackers strategically toggle among BWH, FAW, and PAW attacks. Their main target is to fine-tune the optimal behaviors, which incur maximal rewards. The attackers pinpoint the optimal attacking actions with reinforcement learning, which is formalized into a Markov decision process. The simulation results show that the rewards of the mixed strategy are much higher than that of honest strategy for the attackers. Therefore, the attackers have enough incentives to adopt the mixed strategy. Citing Literature Volume35, Issue12December 2020Pages 2032-2048 RelatedInformation