针对智能家居空间中的设备互联、网络访问方式及业务类型多样性现状,基于IXP425网络处理器和开源Linux Open WRT操作系统,设计支持IPv4和IPv6协议的多模智能融合网关(MMIIG),采用Netfilter/Iptables技术,保证MMIIG的安全性和控制网络流...针对智能家居空间中的设备互联、网络访问方式及业务类型多样性现状,基于IXP425网络处理器和开源Linux Open WRT操作系统,设计支持IPv4和IPv6协议的多模智能融合网关(MMIIG),采用Netfilter/Iptables技术,保证MMIIG的安全性和控制网络流量,在此基础上构建以MMIIG为中心的简易智能家居系统。测试结果表明,该智能家居系统可通过MMIIG使家庭内外网络与电子电器设备相连接,实现远程管理、智能控制、影音共享等功能。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
文摘针对智能家居空间中的设备互联、网络访问方式及业务类型多样性现状,基于IXP425网络处理器和开源Linux Open WRT操作系统,设计支持IPv4和IPv6协议的多模智能融合网关(MMIIG),采用Netfilter/Iptables技术,保证MMIIG的安全性和控制网络流量,在此基础上构建以MMIIG为中心的简易智能家居系统。测试结果表明,该智能家居系统可通过MMIIG使家庭内外网络与电子电器设备相连接,实现远程管理、智能控制、影音共享等功能。
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%.
