随着二维码技术广泛应用于电子票务、银行支票、电子保单等多个领域,二维码的信息泄露和信息篡改等安全问题日益突出.为提高二维码内部信息的安全性能,从对二维码内部信息加密和二维码信息防篡改俩个角度来提高.基于Visual Studio 2008...随着二维码技术广泛应用于电子票务、银行支票、电子保单等多个领域,二维码的信息泄露和信息篡改等安全问题日益突出.为提高二维码内部信息的安全性能,从对二维码内部信息加密和二维码信息防篡改俩个角度来提高.基于Visual Studio 2008 C#平台,设计了一种采用SHA512哈希函数和Rijndael加密算法混合加密的方法,该方法利用Rijndael加密和SHA512数字签名等技术,对Rijndael第一次加密密钥系统随机分配,并对系统随机分配密钥采用二次Rijndael加密防护方法,并通过SHA512对二维码内部信息防篡改校验,达到对二维码信息及其加密密钥的安全保护.在生成QR二维码之前实现了信息加密,并从系统构架、算法原理和实现及安全性能等多个方面进行了测试和分析.分析表明此方法提高了二维码信息的安全性能,达到对密钥高效管理和对信息的多重保护,而在加密后密文信息容量较明文信息有所增加.展开更多
Deep learning(DL)algorithms have been widely used in various security applications to enhance the performances of decision-based models.Malicious data added by an attacker can cause several security and privacy proble...Deep learning(DL)algorithms have been widely used in various security applications to enhance the performances of decision-based models.Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models.The two most common active attacks are poisoning and evasion attacks,which can cause various problems,including wrong prediction and misclassification of decision-based models.Therefore,to design an efficient DL model,it is crucial to mitigate these attacks.In this regard,this study proposes a secure neural network(NN)model that provides data security during model training and testing phases.The main idea is to use cryptographic functions,such as hash function(SHA512)and homomorphic encryption(HE)scheme,to provide authenticity,integrity,and confidentiality of data.The performance of the proposed model is evaluated by experiments based on accuracy,precision,attack detection rate(ADR),and computational cost.The results show that the proposed model has achieved an accuracy of 98%,a precision of 0.97,and an ADR of 98%,even for a large number of attacks.Hence,the proposed model can be used to detect attacks and mitigate the attacker motives.The results also show that the computational cost of the proposed model does not increase with model complexity.展开更多
文摘随着二维码技术广泛应用于电子票务、银行支票、电子保单等多个领域,二维码的信息泄露和信息篡改等安全问题日益突出.为提高二维码内部信息的安全性能,从对二维码内部信息加密和二维码信息防篡改俩个角度来提高.基于Visual Studio 2008 C#平台,设计了一种采用SHA512哈希函数和Rijndael加密算法混合加密的方法,该方法利用Rijndael加密和SHA512数字签名等技术,对Rijndael第一次加密密钥系统随机分配,并对系统随机分配密钥采用二次Rijndael加密防护方法,并通过SHA512对二维码内部信息防篡改校验,达到对二维码信息及其加密密钥的安全保护.在生成QR二维码之前实现了信息加密,并从系统构架、算法原理和实现及安全性能等多个方面进行了测试和分析.分析表明此方法提高了二维码信息的安全性能,达到对密钥高效管理和对信息的多重保护,而在加密后密文信息容量较明文信息有所增加.
文摘Deep learning(DL)algorithms have been widely used in various security applications to enhance the performances of decision-based models.Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models.The two most common active attacks are poisoning and evasion attacks,which can cause various problems,including wrong prediction and misclassification of decision-based models.Therefore,to design an efficient DL model,it is crucial to mitigate these attacks.In this regard,this study proposes a secure neural network(NN)model that provides data security during model training and testing phases.The main idea is to use cryptographic functions,such as hash function(SHA512)and homomorphic encryption(HE)scheme,to provide authenticity,integrity,and confidentiality of data.The performance of the proposed model is evaluated by experiments based on accuracy,precision,attack detection rate(ADR),and computational cost.The results show that the proposed model has achieved an accuracy of 98%,a precision of 0.97,and an ADR of 98%,even for a large number of attacks.Hence,the proposed model can be used to detect attacks and mitigate the attacker motives.The results also show that the computational cost of the proposed model does not increase with model complexity.
