946 research outputs found
On the Strategy and Behavior of Bitcoin Mining with N-attackers
Selfish mining is a well-known mining attack strategy discovered by Eyal and Sirer in 2014. After that, the attackers\u27 strategy space has been extended by many works. These works only analyze the strategy and behavior of one single attacker. The extension of the strategy space is based on the assumption that there is only one attacker in the blockchain network. However, a proof of work blockchain is likely to have several attackers. The attackers can be independent of other attackers instead of sharing information and attacking the blockchain as a whole. During this problem, we are the team who for the first time analyze the miners\u27 behavior in a proof of work blockchain with several attackers by establishing a new model. Based on our model, we extend the attackers\u27 strategy space by proposing a new strategy set publish-n. Meanwhile, we revisit other attacking strategies such as selfish mining and stubborn mining in our model to explore whether these strategies work or not when there are several attackers. We compare the performance of different strategies through relative stale block rate of the attackers. In a proof of work blockchain model with two attackers, strategy publish-n can beat selfish mining by up to 26.3%
A Deep Dive into Blockchain Selfish Mining
This paper studies a fundamental problem regarding the security of blockchain
on how the existence of multiple misbehaving pools influences the profitability
of selfish mining. Each selfish miner maintains a private chain and makes it
public opportunistically for the purpose of acquiring more rewards
incommensurate to his Hashrate. We establish a novel Markov chain model to
characterize all the state transitions of public and private chains. The
minimum requirement of Hashrate together with the minimum delay of being
profitable is derived in close-form. The former reduces to 21.48% with the
symmetric selfish miners, while their competition with asymmetric Hashrates
puts forward a higher requirement of the profitable threshold. The profitable
delay increases with the decrease of the Hashrate of selfish miners, making the
mining pools more cautious on performing selfish mining.Comment: 6 pages, 13 figure
On Cyber Risk Management of Blockchain Networks: A Game Theoretic Approach
Open-access blockchains based on proof-of-work protocols have gained
tremendous popularity for their capabilities of providing decentralized
tamper-proof ledgers and platforms for data-driven autonomous organization.
Nevertheless, the proof-of-work based consensus protocols are vulnerable to
cyber-attacks such as double-spending. In this paper, we propose a novel
approach of cyber risk management for blockchain-based service. In particular,
we adopt the cyber-insurance as an economic tool for neutralizing cyber risks
due to attacks in blockchain networks. We consider a blockchain service market,
which is composed of the infrastructure provider, the blockchain provider, the
cyber-insurer, and the users. The blockchain provider purchases from the
infrastructure provider, e.g., a cloud, the computing resources to maintain the
blockchain consensus, and then offers blockchain services to the users. The
blockchain provider strategizes its investment in the infrastructure and the
service price charged to the users, in order to improve the security of the
blockchain and thus optimize its profit. Meanwhile, the blockchain provider
also purchases a cyber-insurance from the cyber-insurer to protect itself from
the potential damage due to the attacks. In return, the cyber-insurer adjusts
the insurance premium according to the perceived risk level of the blockchain
service. Based on the assumption of rationality for the market entities, we
model the interaction among the blockchain provider, the users, and the
cyber-insurer as a two-level Stackelberg game. Namely, the blockchain provider
and the cyber-insurer lead to set their pricing/investment strategies, and then
the users follow to determine their demand of the blockchain service.
Specifically, we consider the scenario of double-spending attacks and provide a
series of analytical results about the Stackelberg equilibrium in the market
game
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