TY - GEN
T1 - Malicious bitcoin transaction tracing using incidence relation clustering
AU - Zheng, Baokun
AU - Zhu, Liehuang
AU - Shen, Meng
AU - Du, Xiaojiang
AU - Yang, Jing
AU - Gao, Feng
AU - Li, Yandong
AU - Zhang, Chuan
AU - Liu, Sheng
AU - Yin, Shu
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.
PY - 2018
Y1 - 2018
N2 - Since the generation of Bitcoin, it has gained attention of all sectors of the society. Law breakers committed crimes by utilizing the anonymous characteristics of Bitcoin. Recently, how to track malicious Bitcoin transactions has been proposed and studied. To address the challenge, existing solutions have limitations in accuracy, comprehensiveness, and efficiency. In this paper, we study Bitcoin blackmail virus WannaCry event incurred in May 2017. The three Bitcoin addresses disclosed in this blackmail event are only restricted to receivers accepting Bitcoin sent by victims, and no further transaction has been found yet. Therefore, we acquire and verify experimental data by example of similar Bitcoin blackmail virus CryptoLocker occurred in 2013. We focus on how to track malicious Bitcoin transactions, and adopt a new heuristic clustering method to acquire incidence relation between addresses of Bitcoin and improved Louvain clustering algorithm to further acquire incidence relation between users. In addition, through a lot of experiments, we compare the performance of our algorithm with another related work. The new heuristic clustering method can improve comprehensiveness and accuracy of the results. The improved Louvain clustering algorithm can increase working efficiency. Specifically, we propose a method acquiring internal relationship between Bitcoin addresses and users, so as to make Bitcoin transaction deanonymisation possible, and realize a better utilization of Bitcoin in the future.
AB - Since the generation of Bitcoin, it has gained attention of all sectors of the society. Law breakers committed crimes by utilizing the anonymous characteristics of Bitcoin. Recently, how to track malicious Bitcoin transactions has been proposed and studied. To address the challenge, existing solutions have limitations in accuracy, comprehensiveness, and efficiency. In this paper, we study Bitcoin blackmail virus WannaCry event incurred in May 2017. The three Bitcoin addresses disclosed in this blackmail event are only restricted to receivers accepting Bitcoin sent by victims, and no further transaction has been found yet. Therefore, we acquire and verify experimental data by example of similar Bitcoin blackmail virus CryptoLocker occurred in 2013. We focus on how to track malicious Bitcoin transactions, and adopt a new heuristic clustering method to acquire incidence relation between addresses of Bitcoin and improved Louvain clustering algorithm to further acquire incidence relation between users. In addition, through a lot of experiments, we compare the performance of our algorithm with another related work. The new heuristic clustering method can improve comprehensiveness and accuracy of the results. The improved Louvain clustering algorithm can increase working efficiency. Specifically, we propose a method acquiring internal relationship between Bitcoin addresses and users, so as to make Bitcoin transaction deanonymisation possible, and realize a better utilization of Bitcoin in the future.
KW - Bitcoin
KW - Blockchain
KW - Cluster
KW - Incidence relation
UR - http://www.scopus.com/inward/record.url?scp=85047466709&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-90775-8_25
DO - 10.1007/978-3-319-90775-8_25
M3 - Conference contribution
AN - SCOPUS:85047466709
SN - 9783319907741
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 313
EP - 323
BT - Mobile Networks and Management - 9th International Conference, MONAMI 2017, Proceedings
A2 - Wen, Sheng
A2 - Hu, Jiankun
A2 - Khalil, Ibrahim
A2 - Tari, Zahir
PB - Springer Verlag
T2 - 9th International Conference on Mobile Networks and Management, MONAMI 2017
Y2 - 13 December 2017 through 15 December 2017
ER -
