DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Shin, Seungwon | - |
dc.contributor.advisor | 신승원 | - |
dc.contributor.author | Lee, Suyeol | - |
dc.date.accessioned | 2022-04-27T19:31:21Z | - |
dc.date.available | 2022-04-27T19:31:21Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=948734&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/296005 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2021.2,[iv, 37 p. :] | - |
dc.description.abstract | Cryptocurrencies are used as a channel for dealing with illegal funds to criminals. In fact, more than billions-dollar of Bitcoin have been penetrating cryptocurrency exchange every year. Despite the Bitcoin financial forensics' urgency to investigate criminals, existing methods inhere fundamental limitations due to lack of consideration about graph data even though Bitcoin user graph is hypergraph data. To address the limitations of existing methods, we develop a novel Hyperedge Classification method by approximating structure-based edge similarity through graph neural net to detect illegal transactions, represented as hyperedge in the Bitcoin user graph. Moreover, we present a highly scalable graph neural net algorithm by utilizing clustering-based scalable graph neural net and random walk-based sampling method to handle large Bitcoin graph. Based on novel Hyperedge classification, we propose a framework called CENSor, which enables powerful and robust detection than legacy techniques about both illegal entity detection and illegal transaction detection. Compared to the previous graph-based method, our framework succeeds in improving the increment of F-1 score ten times better. We could represent the importance of utilizing proper graph information in Bitcoin analysis by visualizing the Bitcoin cluster graph and Hyperedge-Node switched graph. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Edge Classification▼aHyperedge Classification▼aGraph Neural Network▼aBitcoin Graph▼aBitcoin Transaction▼aBitcoin Cluster | - |
dc.subject | 에지 분류▼a하이퍼에지 분류▼a그래프 뉴럴네트워크▼a비트코인 그래프▼a비트코인 거래▼a비트코인 클러스터 | - |
dc.title | Hyperedge classification method via graph-based deep learning for detecting illicit bitcoin activities | - |
dc.title.alternative | 그래프 기반 딥러닝을 통한 하이퍼에지 분류 방법 및 불법 비트코인 활동 탐지에서의 적용 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 이수열 | - |
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