Responsible Person: Sheng Nan Li
Type of work: Empirical Data Collection and Analysis
Background: Selfish mining is an attack vector on the Bitcoin protocol introduced by Eyal and Sirer  in 2014. A selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks will waste honest miners’ resource and might eventually destroy the system’s security. In the previous works, we propose a statistical test to analyze each miner's behavior in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash . Our method is based on the realization that selfish mining behavior will cause identifiable anomalies in the statistics of miner's successive blocks discovery [2,3]. Additionally, we apply heuristics-based address clustering to improve the detectability of this kind of behavior.
Dataset: In this study, the student needs to collect the simple datasets that include miners and/or mining pools of each block in the main chain of more PoW blockchains analyzed, and some high-quality tag packages for mining pools could improve the detection.
Objective: The thesis goal is to detect suspicious selfish miner in several real-world PoW blockchains such as Bitcoin Gold, Monero, Zcash and Dogecoin.
 Eyal Ittay and Emin Gün Sirer. "Majority is not enough: Bitcoin mining is vulnerable." Communications of the ACM 61.7 (2018): 95-102.
 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.
 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
 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)