SQL Server 2005中的Transact—SQL（T—SQL）语言将提供比之前的版本更强大的功能和灵活性。这些新功能可以改善您的表达能力、查缅性能以及错误管理功能。如使用TRY／CATCH构造进行错误处理、CTE通用表表达式、PIVOT和UNPIVOT运算符、...SQL Server 2005中的Transact—SQL（T—SQL）语言将提供比之前的版本更强大的功能和灵活性。这些新功能可以改善您的表达能力、查缅性能以及错误管理功能。如使用TRY／CATCH构造进行错误处理、CTE通用表表达式、PIVOT和UNPIVOT运算符、SNAPSHOT隔离和DLL触发器方面的增强。展开更多
With the rise of digital transactions, e-Wallets have become prime targets for fraudulent activity. Early detection of suspicious transactions is therefore crucial to protect users and maintain trust in these systems....With the rise of digital transactions, e-Wallets have become prime targets for fraudulent activity. Early detection of suspicious transactions is therefore crucial to protect users and maintain trust in these systems. This article proposes a mathematical model based on a Mixed Integer Program (MIP) to identify and block suspicious transactions. This mathematical approach is designed to analyze e-Wallet transactions in real time by combining integer and continuous decision variables, offering greater flexibility in modeling fraud detection constraints. It considers parameters such as transaction cost, geographical location, type of device used for the transaction, IP address and other potential fraud indicators. Assigning suspicion scores to each transaction enables the model to identify risks that become habitual behavior and mark them as suspicious. Tests carried out on 10,000 digital transactions show that using PMNE significantly improves the detection of fraudulent transactions by identifying the most critical anomalies in terms of accuracy, adaptability and operational efficiency. The model also offers greater accuracy, reducing the number of false positives and false negatives, enabling faster intervention to block truly suspicious transactions.展开更多
Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes.To address these issues,a blockchain cross-chain transaction protection sche...Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes.To address these issues,a blockchain cross-chain transaction protection scheme based on Fully Homomorphic Encryption(FHE)is proposed.In the proposed scheme,the functional relationship is established by Box-Muller,Discrete Gaussian Distribution Function(DGDF)and Uniform Random Distribution Func-tion(URDF)are used to improve the security and efficiency of key generation.Subsequently,the data preprocessing function is introduced to perform cleaning,deduplication,and normalization operations on the transaction data of multi-key signature,and it is classified into interactive data and asset data,so as to perform different homomorphic operations in the FHE encryption stage.Ultimately,in the FHE encryption stage,homomorphic multiplication and homomorphic addition are used targeted for the interactive data and asset data,thereby reducing the computational complexity and enhancing the FHE encryption efficiency.The significance of the proposed scheme is proved by experimental results:Firstly,the multi-key generation function and its specific sampling method and transformation ensure the security and efficiency of key generation.Data preprocessing can also accelerate the FHE encryption process by eliminating invalid data and redundancy,so the FHE encryption efficiency is significantly improved.Secondly,the FHE encryption method based on discrete logarithm problem enhances the security of transaction data and can effectively resist multiple attacks.In addition,the preprocessed data also has good performance in capacity storage.The proposed scheme has significant impacts on key indicators such as encryption efficiency and security,it provides a new reference for blockchain cross-chain transaction protection technology and has an important impact on the security improvement of various cross-chain transaction data.展开更多
文摘SQL Server 2005中的Transact—SQL（T—SQL）语言将提供比之前的版本更强大的功能和灵活性。这些新功能可以改善您的表达能力、查缅性能以及错误管理功能。如使用TRY／CATCH构造进行错误处理、CTE通用表表达式、PIVOT和UNPIVOT运算符、SNAPSHOT隔离和DLL触发器方面的增强。
文摘With the rise of digital transactions, e-Wallets have become prime targets for fraudulent activity. Early detection of suspicious transactions is therefore crucial to protect users and maintain trust in these systems. This article proposes a mathematical model based on a Mixed Integer Program (MIP) to identify and block suspicious transactions. This mathematical approach is designed to analyze e-Wallet transactions in real time by combining integer and continuous decision variables, offering greater flexibility in modeling fraud detection constraints. It considers parameters such as transaction cost, geographical location, type of device used for the transaction, IP address and other potential fraud indicators. Assigning suspicion scores to each transaction enables the model to identify risks that become habitual behavior and mark them as suspicious. Tests carried out on 10,000 digital transactions show that using PMNE significantly improves the detection of fraudulent transactions by identifying the most critical anomalies in terms of accuracy, adaptability and operational efficiency. The model also offers greater accuracy, reducing the number of false positives and false negatives, enabling faster intervention to block truly suspicious transactions.
文摘Low data encryption efficiency and inadequate security are two issues with the current blockchain cross-chain transaction protection schemes.To address these issues,a blockchain cross-chain transaction protection scheme based on Fully Homomorphic Encryption(FHE)is proposed.In the proposed scheme,the functional relationship is established by Box-Muller,Discrete Gaussian Distribution Function(DGDF)and Uniform Random Distribution Func-tion(URDF)are used to improve the security and efficiency of key generation.Subsequently,the data preprocessing function is introduced to perform cleaning,deduplication,and normalization operations on the transaction data of multi-key signature,and it is classified into interactive data and asset data,so as to perform different homomorphic operations in the FHE encryption stage.Ultimately,in the FHE encryption stage,homomorphic multiplication and homomorphic addition are used targeted for the interactive data and asset data,thereby reducing the computational complexity and enhancing the FHE encryption efficiency.The significance of the proposed scheme is proved by experimental results:Firstly,the multi-key generation function and its specific sampling method and transformation ensure the security and efficiency of key generation.Data preprocessing can also accelerate the FHE encryption process by eliminating invalid data and redundancy,so the FHE encryption efficiency is significantly improved.Secondly,the FHE encryption method based on discrete logarithm problem enhances the security of transaction data and can effectively resist multiple attacks.In addition,the preprocessed data also has good performance in capacity storage.The proposed scheme has significant impacts on key indicators such as encryption efficiency and security,it provides a new reference for blockchain cross-chain transaction protection technology and has an important impact on the security improvement of various cross-chain transaction data.
