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Bio-inspired cryptosystem with DNA cryptography and neural networks.

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Title: Bio-inspired cryptosystem with DNA cryptography and neural networks.
Authors: Basu, Sayantani1, Karuppiah, Marimuthu1 k.marimuthu@vit.ac.in, Nasipuri, Mita2, Halder, Anup Kumar2, Radhakrishnan, Niranchana1
Source: Journal of Systems Architecture. Mar2019, Vol. 94, p24-31. 8p.
Subjects: Biologically inspired computing, Cryptography, Artificial neural networks, Machine learning, Genetic code
Abstract: Abstract Bio-Inspired Cryptosystems are a modern form of Cryptography where bio-inspired and machine learning techniques are used for the purpose of securing data. A system has been proposed based on the Central Dogma of Molecular Biology (CDMB) for the Encryption and Decryption Algorithms by simulating the natural processes of Genetic Coding (conversion from binary to DNA bases), Transcription (conversion from DNA to mRNA) and Translation (conversion from mRNA to Protein) as well as the reverse processes to allow for encryption and decryption respectively. All inputs are considered to be in the form of blocks of 16-bits. The final outputs from the blocks can be concatenated to form the final cipher text in the form of protein bases. A Bidirectional Associative Memory Neural Network (BAMNN) has been trained using randomized data for key generation which is capable of saving memory space by remembering and regenerating the sets of keys in a recurrent fashion. The proposed bio-inspired cryptosystem shows competent encryption and decryption times even on large data sizes when compared with existing systems. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Systems Architecture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Biologically+inspired+computing%22">Biologically inspired computing</searchLink><br /><searchLink fieldCode="DE" term="%22Cryptography%22">Cryptography</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+code%22">Genetic code</searchLink>
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  Data: Abstract Bio-Inspired Cryptosystems are a modern form of Cryptography where bio-inspired and machine learning techniques are used for the purpose of securing data. A system has been proposed based on the Central Dogma of Molecular Biology (CDMB) for the Encryption and Decryption Algorithms by simulating the natural processes of Genetic Coding (conversion from binary to DNA bases), Transcription (conversion from DNA to mRNA) and Translation (conversion from mRNA to Protein) as well as the reverse processes to allow for encryption and decryption respectively. All inputs are considered to be in the form of blocks of 16-bits. The final outputs from the blocks can be concatenated to form the final cipher text in the form of protein bases. A Bidirectional Associative Memory Neural Network (BAMNN) has been trained using randomized data for key generation which is capable of saving memory space by remembering and regenerating the sets of keys in a recurrent fashion. The proposed bio-inspired cryptosystem shows competent encryption and decryption times even on large data sizes when compared with existing systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Systems Architecture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.sysarc.2019.02.005
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        Text: English
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        Type: general
      – SubjectFull: Cryptography
        Type: general
      – SubjectFull: Artificial neural networks
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      – SubjectFull: Machine learning
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      – SubjectFull: Genetic code
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              Text: Mar2019
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