Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent...Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent behavior,posing a serious threat to account asset security.For these potential security risks,this paper proposes a hybrid neural network detection method(HNND)that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain.In HNND,the Temporal Transaction Graph Attention Network(T2GAT)is first designed to learn biased aggregation representation of multi-attribute transactions among nodes,which can capture key temporal information from node neighborhood transactions.Then,the Graph Convolutional Network(GCN)is adopted which captures abstract structural features of the transaction network.Further,the Stacked Denoising Autoencode(SDA)is developed to achieve adaptive fusion of thses features from different modules.Moreover,the SDA enhances robustness and generalization ability of node representation,leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts.Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.展开更多
With the application and development of blockchain technology,many problems faced by blockchain traceability are gradually exposed.Such as cross-chain information collaboration,data separation and storage,multisystem,...With the application and development of blockchain technology,many problems faced by blockchain traceability are gradually exposed.Such as cross-chain information collaboration,data separation and storage,multisystem,multi-security domain collaboration,etc.To solve these problems,it is proposed to construct trust domains based on federated chains.The public chain is used as the authorization chain to build a cross-domain data traceability mechanism applicable to multi-domain collaboration.First,the architecture of the blockchain cross-domain model is designed.Combined with the data access strategy and the decision mechanism,the open and transparent judgment of cross-domain permission and cross-domain identity authentication is realized.And the public chain consensus node election mechanism is realized based on PageRank.Then,according to the characteristics of a nonsingle chain structure in the process of data flow,a data retrievalmechanism based on a Bloom filter is designed,and the cross-domain traceability algorithm is given.Finally,the safety and effectiveness of the traceability mechanism are verified by security evaluation and performance analysis.展开更多
文摘Blockchain platform swith the unique characteristics of anonymity,decentralization,and transparency of their transactions,which are faced with abnormal activities such as money laundering,phishing scams,and fraudulent behavior,posing a serious threat to account asset security.For these potential security risks,this paper proposes a hybrid neural network detection method(HNND)that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain.In HNND,the Temporal Transaction Graph Attention Network(T2GAT)is first designed to learn biased aggregation representation of multi-attribute transactions among nodes,which can capture key temporal information from node neighborhood transactions.Then,the Graph Convolutional Network(GCN)is adopted which captures abstract structural features of the transaction network.Further,the Stacked Denoising Autoencode(SDA)is developed to achieve adaptive fusion of thses features from different modules.Moreover,the SDA enhances robustness and generalization ability of node representation,leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts.Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.
文摘With the application and development of blockchain technology,many problems faced by blockchain traceability are gradually exposed.Such as cross-chain information collaboration,data separation and storage,multisystem,multi-security domain collaboration,etc.To solve these problems,it is proposed to construct trust domains based on federated chains.The public chain is used as the authorization chain to build a cross-domain data traceability mechanism applicable to multi-domain collaboration.First,the architecture of the blockchain cross-domain model is designed.Combined with the data access strategy and the decision mechanism,the open and transparent judgment of cross-domain permission and cross-domain identity authentication is realized.And the public chain consensus node election mechanism is realized based on PageRank.Then,according to the characteristics of a nonsingle chain structure in the process of data flow,a data retrievalmechanism based on a Bloom filter is designed,and the cross-domain traceability algorithm is given.Finally,the safety and effectiveness of the traceability mechanism are verified by security evaluation and performance analysis.
