Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조성현 | - |
dc.date.accessioned | 2023-04-25T01:22:45Z | - |
dc.date.available | 2023-04-25T01:22:45Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.citation | SENSORS, v. 23.0, NO. 1, article no. 463, | - |
dc.identifier.issn | 1424-8220;1424-3210 | - |
dc.identifier.uri | https://www.mdpi.com/1424-8220/23/1/463 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/179224 | - |
dc.description.abstract | Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users' assets, threaten the network's reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users' assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC. | - |
dc.description.sponsorship | This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00704, Development of 3D-NET Core Technology for High-Mobility Vehicular Service) and by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00368, Development of the 6G Service Targeted AI/ML-based Autonomous-Regulating Medium Access Control (6G STAR-MAC)). | - |
dc.language | en | - |
dc.publisher | MDPI | - |
dc.subject | blockchain | - |
dc.subject | cryptocurrency | - |
dc.subject | phishing detection | - |
dc.subject | graph learning | - |
dc.subject | heterogeneous graph | - |
dc.title | Graph Learning-Based Blockchain Phishing Account Detection with a Heterogeneous Transaction Graph | - |
dc.type | Article | - |
dc.relation.no | 1 | - |
dc.relation.volume | 23.0 | - |
dc.identifier.doi | 10.3390/s23010463 | - |
dc.relation.journal | SENSORS | - |
dc.contributor.googleauthor | Kim, Jaehyeon | - |
dc.contributor.googleauthor | Lee, Sejong | - |
dc.contributor.googleauthor | Kim, Yushin | - |
dc.contributor.googleauthor | Ahn, Seyoung | - |
dc.contributor.googleauthor | Cho, Sunghyun | - |
dc.sector.campus | E | - |
dc.sector.daehak | 소프트웨어융합대학 | - |
dc.sector.department | 컴퓨터학부 | - |
dc.identifier.pid | chopro | - |
dc.identifier.article | 463 | - |
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