Mind the Mining

Goren, Guy; Spiegelman, Alexander

doi:10.1145/3328526.3329566

Cited by 30 publications

(27 citation statements)

References 11 publications

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“…A wide range of work has also focused on potential mining attacks besides selfish mining. One example is difficulty attacks [23], [47], in which miners can profitably manipulate a chain's difficulty by secretly raising difficulty on their own private chain [4], switching between competing currencies secured by the same mining hardware [47], or pausing mining activities around difficulty adjustment time [23]. This can discourage mining altogether [30].…”

Section: Related Workmentioning

confidence: 99%

“…In what follows, we let S a and S o denote the number of stale blocks mined by the adversary of interest and all the other miners in a single epoch, respectively; a stale block is a block that does not end up on the main chain, and hence does not collect any block reward. Note that the following result relies on the deterministic analysis of [23], which abstracts away the stochastics of the problem.…”

Section: Appendixmentioning

confidence: 99%

“…We use the deterministic analysis of [23], which deals entirely with expectations and abstracts away random block times; it is a good approximation when the epoch duration is high (as in Bitcoin). Let the number of agents in the blockchain be k. We denote the number of main chain blocks generated by the attacker during the ith epoch as B a (i) (assuming 1 ≤ i ≤ n) and the number of stale blocks generated by the attacker as S a (i); we suppress the notation p for simplicity.…”

Section: Appendixmentioning

confidence: 99%

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SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning

Hou¹,

Zhou²,

Ji³

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

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Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested. In this work, we propose SquirRL, a framework for using deep reinforcement learning to analyze attacks on blockchain incentive mechanisms. We demonstrate SquirRL's power by first recovering known attacks: (1) the optimal selfish mining attack in Bitcoin [56], and (2) the Nash equilibrium in block withholding attacks [18]. We also use SquirRL to obtain several novel empirical results. First, we discover a counterintuitive flaw in the widely used rushing adversary model when applied to multi-agent Markov games with incomplete information. Second, we demonstrate that the optimal selfish mining strategy identified in [56] is actually not a Nash equilibrium in the multi-agent selfish mining setting. In fact, our results suggest (but do not prove) that when more than two competing agents engage in selfish mining, there is no profitable Nash equilibrium. This is consistent with the lack of observed selfish mining in the wild. Third, we find a novel attack on a simplified version of Ethereum's finalization mechanism, Casper the Friendly Finality Gadget (FFG) that allows a strategic agent to amplify her rewards by up to 30%. Notably, [12] shows that honest voting is a Nash equilibrium in Casper FFG; our attack shows that when Casper FFG is composed with selfish mining, this is no longer the case. Altogether, our experiments demonstrate SquirRL's flexibility and promise as a framework for studying attack settings that have thus far eluded theoretical and empirical understanding.

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Section: Related Workmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

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SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning

Hou¹,

Zhou²,

Ji³

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

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“…There is a vast literature investigating game theoretical aspects of blockchain systems (Kiayias et al, 2016;Huberman et al, 2017;Abdellatif and Abdelmouttalib, 2018;Ma et al, 2018;Papadis et al, 2018;Spiegelman et al, 2018;Azouvi and Hicks, 2019;Goren and Spiegelman, 2019;Liu et al, 2019a,b;Wang et al, 2019). Nonetheless, the literature on congestion games applied to such systems is scarce.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, in contrast, we focus on the competition between miners. Spiegelman et al (2018) adopted the framework of congestion games to model competition between miners of multiples cryptocurrencies who try to maximize utilities by choosing which puzzle (cryptocurrency) to mine (the work was then extended at Goren and Spiegelman, 2019). The authors prove that there is no standard potential function for the game they propose, but that an ordinal potential always exists, implying that best response converges to a pure Nash equilibrium.…”

Section: Related Workmentioning

confidence: 99%

Blockchain Competition Between Miners: A Game Theoretic Perspective

et al. 2020

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We model the competition over mining resources and over several cryptocurrencies as a non-cooperative game. Leveraging results about congestion games, we establish conditions for the existence of pure Nash equilibria and provide efficient algorithms for finding such equilibria. We account for multiple system models, varying according to the way that mining resources are allocated and shared and according to the granularity at which mining puzzle complexity is adjusted. When constraints on resources are included, the resulting game is a constrained resource allocation game for which we characterize a normalized Nash equilibrium. Under the proposed models, we provide structural properties of the corresponding types of equilibrium, e.g., establishing conditions under which at most two mining infrastructures will be active or under which no miners will have incentives to mine a given cryptocurrency.

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