4.7 Article

Semi-selfish mining based on hidden Markov decision process

期刊

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
卷 36, 期 7, 页码 3596-3612

出版社

WILEY
DOI: 10.1002/int.22428

关键词

hidden markov decision process; mining revenues; selfish mining

资金

  1. National Natural Science Foundation of China [62072273]

向作者/读者索取更多资源

Selfish mining attacks aim to obtain higher revenues compared with honest parties by exploiting vulnerabilities in the consensus mechanism, but are impractical due to high forking rates leading honest parties to exit the system. An improved selfish mining approach based on hidden Markov decision processes (SMHMDP) is proposed, which can balance revenues and forking rates by allowing semi-selfish miners to mine on the public chain with a small probability rho. Simulation results show that SMHMDP can benefit selfish miners within an acceptable forking rate without becoming an armchair strategist.
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 rho. 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.

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