TY - GEN
T1 - Multi-Class Bitcoin-Enabled Service Identification Based on Transaction History Summarization
AU - Toyoda, Kentaroh
AU - Ohtsuki, Tomoaki
AU - Mathiopoulos, P. Takis
N1 - Funding Information:
This work is partly supported by the Grant KAKENHI (No.18K18162) from Ministry of Education, Sport, Science and Technology, Japan.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7
Y1 - 2018/7
N2 - In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.
AB - In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.
UR - https://www.scopus.com/pages/publications/85062837186
UR - https://www.scopus.com/pages/publications/85062837186#tab=citedBy
U2 - 10.1109/Cybermatics_2018.2018.00208
DO - 10.1109/Cybermatics_2018.2018.00208
M3 - Conference contribution
AN - SCOPUS:85062837186
T3 - Proceedings - IEEE 2018 International Congress on Cybermatics: 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018
SP - 1153
EP - 1160
BT - Proceedings - IEEE 2018 International Congress on Cybermatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Congress on Conferences on Internet of Things, 14th IEEE International Conference on Green Computing and Communications, 11th IEEE International Conference on Cyber, Physical and Social Computing, 4th IEEE International Conference on Smart Data, 1st IEEE International Conference on Blockchain and 18th IEEE International Conference on Computer and Information Technology, iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018
Y2 - 30 July 2018 through 3 August 2018
ER -