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MMCNN-ZO: Modified multi-scale convolutional neural network-based zebra optimization for enhancing data encryption system

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Abstract

Cloud computing (CC) is a rapidly growing technology that is deployed by various organizations due to the high level of scalability, elasticity, and virtualization it offers. However, security-related challenges arise due to insecure access control points. Security models in the cloud such as authentication, data recovery, confidentiality, accessibility, and data integrity. This includes the deployment model, barriers in cloud computing, cloud services, and security issues. Data security can be improved via the usage of encryption and decryption keys during transmission. However, the existing techniques often suffered from improved time consumption and complexity in handling more users. This paper mainly focuses on the data security issue with the outsourced sensitive data in the cloud server by designing a novel Modified Multi-scale Convolutional Neural Network-based Zebra Optimization (MMCNN-ZO) approach to improve the cloud data sharing system. The proposed MMCNN-ZO approach safeguards the crucial data effectively and outputs in the form of cipher text/image. Here, the weight functions of the MMCNN are tuned mechanically using the ZO algorithm for the enhanced encryption process. The decryption process is performed to obtain the data in user understandable format. These procedures make the proposed system effectively secure the data content by protecting against intrusions. The efficacy of the proposed MMCNN-ZO method is investigated using diverse evaluation indicators namely throughput, decryption, and encryption time. The proposed MMCNN-ZO algorithm achieved improvements in terms of throughput, encryption time, and decryption time of 7450 bits/sec, 4.82 ms, and 4.86 ms respectively compared to other existing methods of SVM, ANN, BPPSVC, and GBCRP. The experimental results inherit the better performances of the suggested MMCNN-ZO approach over other methods in terms of all metrics.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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References

  1. Rehman MU, Shafique A, Khan KH, Hazzazi MM (2023) Efficient and secure image encryption using key substitution process with discrete wavelet transform. J King Saud Univ-Comput Inform Sci 101613

  2. Hazzazi MM, Attuluri S, Bassfar Z, Joshi K (2023) A novel cipher-based data encryption with galois field theory. Sensors 23(6):3287

    Article  Google Scholar 

  3. Nadeem M, Arshad A, Riaz S, Zahra SW, Dutta AK, Al Moteri M, Almotairi S (2023) An efficient technique to prevent data misuse with matrix cipher encryption algorithms. Comput Mater Contin 74(2)

  4. Sutradhar S, Karforma S, Bose R, Roy S (2023) A dynamic step-wise tiny encryption algorithm with fruit fly optimization for quality of service improvement in healthcare. Healthc Anal 3:100177

    Article  Google Scholar 

  5. Sodhro AH, Pirbhulal S, Muzammal M, Zongwei L (2020) Towards blockchain-enabled security technique for industrial internet of things based decentralized applications. J Grid Comput 18(4):615–628

    Article  Google Scholar 

  6. Kumar P, Gupta GP, Tripathi R (2021) TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning. J Syst Architect 115:101954

    Article  Google Scholar 

  7. Nguyen DC, Pathirana PN, Ding M, Seneviratne A (2020) Integration of blockchain and cloud of things: Architecture, applications and challenges. IEEE Commun Surv Tutor 22(4):2521–2549

    Article  Google Scholar 

  8. Ahmad S, Mehfuz S (2023) Efficient time-oriented latency-based secure data encryption for cloud storage. Cyber Secur Appl 100027

  9. Oktian YE, Witanto EN, Lee SG (2021) A conceptual architecture in decentralizing computing, storage, and networking aspect of IoT infrastructure. IoT 2(2):205–221

    Article  Google Scholar 

  10. Kadry H, Farouk A, Zanaty EA, Reyad O (2023) Intrusion detection model using optimized quantum neural network and elliptical curve cryptography for data security. Alex Eng J 71:491–500

    Article  Google Scholar 

  11. Samanta D, Alahmadi AH, Karthikeyan MP, Khan MZ, Banerjee A, Dalapati GK, Ramakrishna S (2021) Cipher block chaining support vector machine for secured decentralized cloud enabled intelligent IoT architecture. IEEE Access 9:98013–98025

    Article  Google Scholar 

  12. Mrabet H, Alhomoud A, Jemai A, Trentesaux D (2022) A secured industrial Internet-of-Things architecture based on blockchain technology and machine learning for sensor access control systems in smart manufacturing. Appl Sci 12(9):4641

    Article  Google Scholar 

  13. Singh AK, Saxena D (2022) A cryptography and machine learning based authentication for secure data-sharing in federated cloud services environment. J Appl Secur Res 17(3):385–412

    Article  Google Scholar 

  14. Sana MU, Li Z, Javaid F, Liaqat HB, Ali MU (2021) Enhanced security in cloud computing using neural network and encryption. IEEE Access 9:145785–145799

    Article  Google Scholar 

  15. Meena U, Sharma P (2023) An improved blockchain based encryption scheme for secure routing in wireless sensor network using machine learning technique. Trans Emerg Telecommun Technol 34(3):e4713

    Article  Google Scholar 

  16. Gupta R, Saxena D, Gupta I, Singh AK (2022) Differential and triphase adaptive learning-based privacy-preserving model for medical data in cloud environment. IEEE Netw Lett 4(4):217–221

    Article  Google Scholar 

  17. Saxena D, Singh AK (2022) A high availability management model based on VM significance ranking and resource estimation for cloud applications. IEEE Trans Serv Comput

  18. Smahi A, Xia Q, Xia H, Sulemana N, Fateh AA, Gao J, Du X, Guizani M (2020) A blockchainized privacy-preserving support vector machine classification on mobile crowd sensed data. Pervasive Mob Comput 66:101195

    Article  Google Scholar 

  19. Shen M, Tang X, Zhu L, Du X, Guizani M (2019) Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities. IEEE Internet Things J 6(5):7702–7712

    Article  Google Scholar 

  20. Singh SK, Rathore S, Park JH (2020) Blockiotintelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence. Futur Gener Comput Syst 110:721–743

    Article  Google Scholar 

  21. Ghazal TM, Hasan MK, Abdullah SNHS, Bakar KAA, Al Hamadi H (2020) Private blockchain-based encryption framework using computational intelligence approach. Egypt Inform J

  22. Rahman MA, Hossain MS, Islam MS, Alrajeh NA, Muhammad G (2020) Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach. IEEE Access 8:205071–205087. https://doi.org/10.1109/ACCESS.2020.3037474

    Article  Google Scholar 

  23. Chen X, Zhang H, Song J, Guan J, Li J, He Z (2022) Micro-motion classification of flying bird and rotor drones via data augmentation and modified multi-scale CNN. Remote Sens 14(5):1107

    Article  Google Scholar 

  24. Trojovská E, Dehghani M, Trojovský P (2022) Zebra optimization algorithm: A new bio-inspired optimization algorithm for solving optimization algorithm. IEEE Access 10:49445–49473

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, S.A.Engineering College, Chennai, India

    Anuradha M

  2. Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, India

    Jean Justus J

  3. Department of Information Technology, Pentecost University, Accra, Ghana

    Mary Immaculate Sheela L

Authors
  1. Anuradha M
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  2. Jean Justus J
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  3. Mary Immaculate Sheela L
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Contributions

AM, JJJ, MISL agreed on the content of the study. AM, JJJ, MISL collected all the data for analysis. AM, JJJ, MISL agreed on the methodology. AM, JJJ, MISL completed the analysis based on agreed steps. Results and conclusions are discussed and written together. All authors read and approved the final manuscript.

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Correspondence to Anuradha M.

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M, A., J, J.J. & L, M.I.S. MMCNN-ZO: Modified multi-scale convolutional neural network-based zebra optimization for enhancing data encryption system. Peer-to-Peer Netw. Appl. 17, 924–943 (2024). https://doi.org/10.1007/s12083-023-01592-9

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  • DOI: https://doi.org/10.1007/s12083-023-01592-9

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