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Isomorphic encryption and coupled ANN with Mealy machine: a cutting edge data security model for cloud computing environment

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Abstract

Cloud computing is defined as the distribution of computing including hardware and software to the consumer through the Internet. In the era of ICT, cloud computing has been influenced by many industries including technology, business, management, logistics and numerous other industry. But some new kinds of risks and vulnerabilities exist in cloud environment. Users of cloud services are under constant threat. Hence, security-related risks are the main disadvantage of cloud computing. The aim of this paper is to enhance the cloud security by designing a secure cryptosystem based on AI. We have emphasized on secure key generation algorithm based on coupled artificial neural network with Mealy machine, genetic algorithm and weight vector-based authentication mechanism. We have used coupled multilayer feedforward neural network, Mealy machine and genetic algorithm for key generation. Machine learning is done ‘n’ times between two ANNs, and after several steps, we have generated a secret key for encryption. A novel key wrapping protocol has also been introduced using one-way function. For encryption and decryption, we have used the concept of isomorphism in vector space and XOR operation with double encryption key. Thus, our paper is equipped with different types of new concepts. Varieties of experimental results and analysis prove the efficiency and robustness of our technique in the field of cryptography.

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Authors and Affiliations

  1. Department of Computer Science, M.U.C. Women’s College, Purba Bardhaman, WB, India

    Anirban Bhowmik

  2. Department of Computer Science, The University of Burdwan, Purba Bardhaman, WB, India

    Sunil Karforma

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  1. Anirban Bhowmik
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  2. Sunil Karforma
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Correspondence to Anirban Bhowmik.

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Bhowmik, A., Karforma, S. Isomorphic encryption and coupled ANN with Mealy machine: a cutting edge data security model for cloud computing environment. Knowl Inf Syst 65, 133–162 (2023). https://doi.org/10.1007/s10115-022-01760-y

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  • DOI: https://doi.org/10.1007/s10115-022-01760-y

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