2024年10月2日,国际货币基金组织、世界银行集团和世界卫生组织的负责人就大流行病防范合作达成了“开展协调的基本原则”。这一合作将通过国际货币基金组织的韧性与可持续性信托(Resilience and Sustainability Trust,RST)、世界银行...2024年10月2日,国际货币基金组织、世界银行集团和世界卫生组织的负责人就大流行病防范合作达成了“开展协调的基本原则”。这一合作将通过国际货币基金组织的韧性与可持续性信托(Resilience and Sustainability Trust,RST)、世界银行集团的资金和技术支持以及世界卫生组织的技术和业务专长,为各国预防、发现和应对公共卫生威胁提供支持。展开更多
Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services ope...Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.展开更多
文摘2024年10月2日,国际货币基金组织、世界银行集团和世界卫生组织的负责人就大流行病防范合作达成了“开展协调的基本原则”。这一合作将通过国际货币基金组织的韧性与可持续性信托(Resilience and Sustainability Trust,RST)、世界银行集团的资金和技术支持以及世界卫生组织的技术和业务专长,为各国预防、发现和应对公共卫生威胁提供支持。
基金sponsored by the National Natural Science Foundation of China Nos.62172353,62302114 and U20B2046Future Network Scientific Research Fund Project No.FNSRFP-2021-YB-48Innovation Fund Program of the Engineering Research Center for Integration and Application of Digital Learning Technology of Ministry of Education No.1221045。
文摘Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.
