Responsible Person: Qianyu Liu
Keywords: Attacker identifying, Transactions, Bitcoin
Many cryptocurrency iholders believe that their anonymity and security are well protected and that hackers can't infiltrate their transactions. However, this is not the case. Some attacks, such as dust attacks or transactional malleability attacks, are not only hurt the security, but can also cause congestion in the Bitcoin network. In this project, we will locate attackers in the transaction network, study their pre-attack or early attack transaction behavior by machine learning, and then identify more potential attackers on the bitcoin network.
The main research question to be addressed is: Miner's behavior study.
 Yin H H S , Langenheldt K , Harlev M , et al. Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain[J]. Journal of management information systems, 2019, 36(1):37-73.
 G. Ebrahimpour, M. S. Haghighi and M. Alazab, "Can Blockchain be Trusted in Industry 4.0? Study of a Novel Misleading Attack on Bitcoin," in IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 8307-8315, Nov. 2022, doi: 10.1109/TII.2022.3142036.
 Li, Sheng-Nan and Campajola, Carlo and Tessone, Claudio, " Twisted by the Tools: Detection of Selfish Anomalies in Proof-of-work Mining," DOI:10.48550/arXiv.2208.05748