Level: Master Level
Responsible Person: Sheng Nan Li
Type of work: Agent-based Modelling

Background: Selfish mining is an attack vector on the Bitcoin protocol introduced by Eyal and Sirer [1] in 2014. A selfish miner may achieve this by keeping newly mined blocks private, and only broadcasts the private blocks as honest miners catch up to the private chain's height. If a selfish miner is relatively more profitable, they can invest more resources into computational hardware, further increasing their share of the network's computational resources. This cycle may eventually culminate in the selfish miner controlling the majority of the network and thus breaking system’s incentive compatibility.
Methodology: In our previous works, we have already done some empirical study [2-4] and stochastic modelling [5] of selfish mining in PoW-based blockchain systems. Based on all the current results, it’s meaningful to further research the robustness of detection method and the effect of selfish mining behaviors, especially, when miners located with different centrality in peer-to-peer network.
Objective: The thesis is extended research on modelling the effect of network topology and delay on the robustness of detection of selfish miners. Another goal of this thesis is to analyse the influence of the location on selfish miners’ profitability and on the consensus of the PoW-based blockchain systems.

Reference:
[1] Eyal Ittay and Emin Gün Sirer. "Majority is not enough: Bitcoin mining is vulnerable." Communications of the ACM 61.7 (2018): 95-102.
[2] Sheng-Nan Li, Zhao Yang, and Claudio J. Tessone. ''Mining blocks in a row: A statistical study of fairness in Bitcoin mining." 2020 IEEE international conference on blockchain and cryptocurrency (ICBC). IEEE, 2020.
[3] Sheng-Nan Li, Zhao Yang, and Claudio J. Tessone. ''Proof-of-work cryptocurrency mining: a statistical approach to fairness." 2020 IEEE/CIC international conference on communications in China (ICCC workshops). IEEE, 2020
[4] Sheng-Nan Li, Carlo Campajola, and Claudio J. Tessone. "Twisted by the Pools: Detection of Selfish Anomalies in Proof-of-Work Mining." arXiv preprint arXiv:2208.05748 (2022)
[5] Caspar Schwarz-Schilling, Sheng-Nan Li, and Claudio J. Tessone. "Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols." Journal of Cybersecurity and Privacy 2.2 (2022): 292-310.