Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system's resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin's dependability.

Automated summary

Selfish mining is a malicious attack in the blockchain-based bitcoin system, in which attackers collect unfair rewards by withholding blocks. Previous research on selfish mining mainly focused on cryptography design and detection of malicious behavior using different approaches. This paper proposes two network-wide defensive strategies, DDAA and ALP, aimed at disincentivizing selfish miners and increasing the system's resilience. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability, and statistical analysis evaluates the feasibility of the proposed strategies. The DDAA method is found to be the most effective in improving bitcoin's dependability compared to an existing timestamp-based defense strategy.