A Security Detection Model for Selfish Mining Attack As a new technology, the xe2x80x9cdecentralizationxe2x80x9d of blockchain has been paid more and more attention. With the increasingly wide range of applications of blockchain technology, its security has become a bottleneck restricting the development. In order to solve the problem, the blockchain structure characteristic can be used to build a security detection model. Taking 51% attack detection as an example, the relationship among attack state, attack intensity and attack time was analyzed. However, because of the variety of blockchain attacks, the test of only one attack cannot verify the effectiveness of the detection model. On the basis of the existing work, this paper studies the security of selfish mining attack under the existing detection model and compares it with 51% attack, and finds that the attack block and honest block are related to the ability of the attacker. When the attackerxe2x80x99s ability exceeds half of the total ability, the attack block is more than the honest block. However, for the number of system states, there is a big difference between 51% attack and selfish mining attack. Specifically, in selfish mining attack, when the attackerxe2x80x99s ability exceeds 25%, the number of states tends to stabilize. While in 51% attack, the attackerxe2x80x99s ability needs to exceed 50% in order to stabilize. This shows that the detection model can well distinguish 51% attack and selfish mining attack. According to this characteristic, we can further construct an effective alarm mechanism.

Selfish Mining in Ethereum As the second largest cryptocurrency by market capitalization and todayu0027s biggest decentralized platform that runs smart contracts, Ethereum has received much attention from both industry and academia. Nevertheless, there exist very few studies about the security of its mining strategies, especially from the selfish mining perspective. In this paper, we aim to fill this research gap by analyzing selfish mining in Ethereum and understanding its potential threat. First, we introduce a 2dimensional Markov process to model the behavior of a selfish mining strategy inspired by a Bitcoin mining strategy proposed by Eyal and Sirer. Second, we derive the stationary distribution of our Markov model and compute longterm average mining rewards. This allows us to determine the threshold of computational power that makes selfish mining profitable in Ethereum. We find that this threshold is lower than that in Bitcoin mining (which is 25% as discovered by Eyal and Sirer), suggesting that Ethereum is more vulnerable to selfish mining than Bitcoin.