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Efficient Cipher Scheme for Hybrid Models with Internal Structure Modification


Affiliations
1 Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
 

Data confidentiality (DC) became an essential security service required during data transmission or storing sensitive data over the network in every application. A rise in security attacks causing several security services are required to be implemented for preventing both passive and active attacks. DC security service is most common and based on a complex reversible mathematical calculation recognized as cipher algorithm. The confidentiality of information kept in digital mode, as well as its simple accessing at any moment, has become a significant phenomenon. However, many applications put constrained to develop cipher models that can provide better performance while maintaining minimum security level. In this regard, several hybrid models have been developed and discussed in the literature based on cascading which provides enhanced security but increases system performance due to the running of multiple algorithms. The aim of the proposed scheme is to improve the system performance of the cascaded hybrid model by doing modifications in the internal structure of each algorithm used in the model by reducing the number of rounds required for doing encryption and decryption. Each algorithm used in cascading in hybrid model uses a different key generated using the SHA256 algorithm which helps to maintain the security level of the hybrid cryptography model. Finally, we conduct the performance analysis of the existing hybrid cryptography model with the proposed cipher scheme having a reduced number of rounds. The proposed scheme shows better performance of the hybrid model compared with its corresponding existing model.

Keywords

Data Confidentiality, Hybrid Cryptography, Internal Modification, Cascaded Encryptions, Secure Hash Algorithm.
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  • Efficient Cipher Scheme for Hybrid Models with Internal Structure Modification

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Authors

Pravin Soni
Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
Rahul Malik
Department of Computer Science and Engineering, Lovely Professional University, Punjab, India

Abstract


Data confidentiality (DC) became an essential security service required during data transmission or storing sensitive data over the network in every application. A rise in security attacks causing several security services are required to be implemented for preventing both passive and active attacks. DC security service is most common and based on a complex reversible mathematical calculation recognized as cipher algorithm. The confidentiality of information kept in digital mode, as well as its simple accessing at any moment, has become a significant phenomenon. However, many applications put constrained to develop cipher models that can provide better performance while maintaining minimum security level. In this regard, several hybrid models have been developed and discussed in the literature based on cascading which provides enhanced security but increases system performance due to the running of multiple algorithms. The aim of the proposed scheme is to improve the system performance of the cascaded hybrid model by doing modifications in the internal structure of each algorithm used in the model by reducing the number of rounds required for doing encryption and decryption. Each algorithm used in cascading in hybrid model uses a different key generated using the SHA256 algorithm which helps to maintain the security level of the hybrid cryptography model. Finally, we conduct the performance analysis of the existing hybrid cryptography model with the proposed cipher scheme having a reduced number of rounds. The proposed scheme shows better performance of the hybrid model compared with its corresponding existing model.

Keywords


Data Confidentiality, Hybrid Cryptography, Internal Modification, Cascaded Encryptions, Secure Hash Algorithm.

References





DOI: https://doi.org/10.22247/ijcna%2F2021%2F209990