Skip to main content

Advertisement

Log in

SDESA: secure cloud computing with gradient deep belief network and congruential advanced encryption

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With the aim to enhance cloud security with higher data confidentiality rate and integrity, we propose a novel technique called Stochastic Deep Encryption Standard Algorithm (SDESA). After the successful registration of the cloud user with the Cloud Server, the authentication process is executed using a Stochastic Nesterov Gradient Piecewise Deep Belief Network model, which distinguishes between authorized and unauthorized users with the greatest accuracy and least time. Gestalt pattern matching method is used to find legitimate users. Stochastic Nesterov gradient approach is adapted to decrease the classification error, thus boosting the attack detection accuracy. Linear Congruential Ephemeral Advanced Encryption Standard Algorithm, which uses the Linear Congruential Ephemeral Secret Random Key for data encryption/ decryption to protect data by preventing unauthorized access, is finally introduced to guarantee the data confidentiality and integrity. The results indicate that SDESA outperforms conventional methods in terms of various performance metrics under different scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Fig. 6
Fig. 7
Algorithm 2
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. 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 

  2. Alam M, Shahid M, Mustajab S (2024) Security prioritized multiple workflow allocation model under precedence constraints in cloud computing environment. Clust Comput 27(1):341–376

    Article  Google Scholar 

  3. Prabhakaran V, Kulandasamy A (2021) Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud. Comput Intell 37(1):344–370

    Article  MathSciNet  Google Scholar 

  4. Arasan A, Sadaiyandi R, Al-Turjman F, Rajasekaran AS, Karuppuswamy KS (2024) Computationally efficient and secure anonymous authentication scheme for cloud users. Pers Ubiquit Comput 28(1):111–121

    Article  Google Scholar 

  5. Tahir M, Sardaraz M, Mehmood Z, Muhammad S (2021) CryptoGA: a cryptosystem based on genetic algorithm for cloud data security. Cluster Comput 24:739–752

    Article  Google Scholar 

  6. Thabit F, Alhomdy S, Jagtap S (2021) A new data security algorithm for the cloud computing based on genetics techniques and logical-mathematical functions. Int J Intell Netw 2:18–33

    Google Scholar 

  7. Aldallal A, Alisa F (2021) Effective intrusion detection system to secure data in cloud using machine learning. Symmetry 13(12):1–26

    Article  Google Scholar 

  8. Islam MDA, Madria SK (2022) Attribute-based encryption scheme for secure multi-group data sharing in cloud. IEEE Trans Serv Comput 15(4):2158–2172

    Article  Google Scholar 

  9. Deore B, Bhosale S (2022) Hybrid optimization enabled robust CNN-LSTM technique for network intrusion detection. IEEE Access 10:65611–65622

    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(1):491–500

    Article  Google Scholar 

  11. Ho S, Al Jufout S, Dajani K, Mozumdar M (2021) A novel intrusion detection model for detecting known and innovative cyberattacks using convolutional neural network. IEEE Open J Comput Soc 2:14–25

    Article  Google Scholar 

  12. Osamy W, Salim A, Khedr AM, El-Sawy AA (2021) IDCT: intelligent data collection technique for IoT-enabled heterogeneous wireless sensor networks in smart environments. IEEE Sens J 21(18):21099–21112

    Article  Google Scholar 

  13. Khedr AM, Aghbari ZAL, Kamel I (2018) Privacy preserving decomposable mining association rules on distributed data. Int J Eng Technol 7(3.13):157–164

    Article  Google Scholar 

  14. Khedr AM, Bhatnagar RK (2014) New algorithm for clustering distributed data using K-means. Comput Inf 33(4):943–964

    Google Scholar 

  15. Khedr AM, Mahmoud R (2012) Agents for integrating distributed data for function computations. Comput Inf 31:1101–1125

    MathSciNet  Google Scholar 

  16. Anandhi V, Vinod P, Menon VG, Aditya KM (2022) Performance evaluation of deep neural network on malware detection: visual feature approach. Cluster Comput 25:4601–4615

    Article  Google Scholar 

  17. Mendes R, Oliveira T, Cogo V, Neves N, Bessani A (2021) Charon: a secure cloud-of-clouds system for storing and sharing big data. IEEE Trans Cloud Comput 9(4):1349–1361

    Article  Google Scholar 

  18. Halbouni A, Gunawan TS, Habaebi MH, Halbouni M, Kartiwi M, Ahmad R (2022) CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE Access 10:99837–99849

    Article  Google Scholar 

  19. Khedr, A. M., Osamy, W., Ahmed, S., Abdel-Aziz, S. (2019) Privacy-preserving data mining approach for IoT based WSN in smart city. International Journal of Advanced Computer Science and Applications, Science and Information (SAI) Organization Limited 10(8)

  20. Butt UA, Mehmood M, Shah SBH, Amin R, Shaukat MW, Raza SM, Piran MJ (2020) A review of machine learning algorithms for cloud computing security. Electronics 9(9):1379

    Article  Google Scholar 

  21. Nassif AB, Talib MA, Nasir Q, Albadani H, Dakalbab FM (2021) Machine learning for cloud security: a systematic review. IEEE Access 9:20717–20735

    Article  Google Scholar 

  22. Jyoti A, Chauhan RK (2022) A blockchain and smart contract-based data provenance collection and storing in cloud environment. Wireless Netw 28(4):1541–1562

    Article  Google Scholar 

  23. Man Z, Li J, Di X, Zhang R, Li X, Sun X (2023) Research on cloud data encryption algorithm based on bidirectional activation neural network. Inf Sci 622:629–651

    Article  Google Scholar 

  24. Fan Y, Zhang W, Bai J, Lei X, Li K (2023) Privacy-preserving deep learning on big data in cloud. China Communications 1–11

  25. Khedr AM (2020) EDCP: effective decomposable closest pair algorithm for distributed databases. Eng Lett 28(3):930–938

    Google Scholar 

  26. Jain S, Pawar PM, Muthalagu R (2022) Hybrid intelligent intrusion detection system for internet of things. Telematics Inf Rep 8:1–7

    Google Scholar 

  27. Ramachandra MN, Rao MS, Lai WC, Parameshachari BD, Babu JA, Hemalatha KL (2022) An efficient and secure big data storage in cloud environment by using triple data encryption standard. Big Data Cognit Comput 6(4):1–20

    Article  Google Scholar 

  28. Gao J, Gan L, Buschendorf F, Zhang L, Liu H, Li P, Dong X, Tao L (2021) Omni SCADA intrusion detection using deep learning algorithms. IEEE Internet Things J 8(2):951–961

    Article  Google Scholar 

  29. Janabi AH, Kanakis T, Johnson M (2021) Convolutional neural network based algorithm for early warning proactive system security in software defined networks. IEEE Access 10:14301–14310

    Article  Google Scholar 

  30. Liu L, Wang P, Lin J, Liu L (2020) Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE Access 9:7550–7563

    Article  Google Scholar 

  31. Mondal A, Goswami RT (2021) Enhanced Honeypot cryptographic scheme and privacy preservation for an effective prediction in cloud security. Microprocess Microsyst 81:1–10

    Article  Google Scholar 

  32. Salim A, Osamy W, Khedr AM, Aziz A, Abdel-Mageed M (2020) A secure data gathering scheme based on properties of primes and compressive sensing for IoT-based WSNs. IEEE Sens J 211(4):5553–5571

    Article  Google Scholar 

  33. Salim A, Ismail A, Osamy W, Khedr MA (2021) Compressive sensing based secure data aggregation scheme for IoT based WSN applications. PloS one 16(12):e0260634

    Article  Google Scholar 

  34. Salim A, Osamy W, Aziz A, Khedr AM (2022) SEEDGT: secure and energy-efficient data gathering technique for IoT applications based WSNs. J Netw Comput Appl 202:103353

    Article  Google Scholar 

  35. Mohiyuddin A, Javed AR, Chakraborty C, Rizwan M, Shabbir M, Nebhen J (2022) Secure cloud storage for medical IoT data using adaptive neuro-fuzzy inference system. Int J Fuzzy Syst 24(2):1203–1215

    Article  Google Scholar 

  36. Abid R, Iwendi C, Javed AR, Rizwan M, Jalil Z, Anajemba JH, Biamba C (2023) RETRACTED ARTICLE: an optimised homomorphic CRT-RSA algorithm for secure and efficient communication. Pers Ubiquitous Comput 27(3):1405–1418

    Article  Google Scholar 

  37. kram AA, Javed AR, Rizwan M, Abid R, Crichigno J, Srivastava G, Mobile Cloud Computing Framework for Securing Data. In: Proceedings of the 2021 44th International Conference on Telecommunications and Signal Processing (TSP), Brno, Czech Republic, 26–28 July 2021; pp 309–315

  38. Jouini M, Rabai LBA (2019) A security framework for secure cloud computing environments. In: Cloud security: Concepts, Methodologies, Tools, and Applications; IGI Global: Hershey, PA, USA, pp 249–263

  39. Dubey K, Shams MY, Sharma SC, Alarifi A, Amoon M, Nasr AA (2019) A management system for servicing multi-organizations on community cloud model in secure cloud environment. IEEE Access 7:159535–159546

    Article  Google Scholar 

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

Authors

Contributions

To this study, the authors contributed equally. The final manuscript is read and approved by all authors.

Corresponding author

Correspondence to P. V. Pravija Raj.

Ethics declarations

Conflict of interest

Regarding the publishing of this work, the authors affirm that there are no Conflict of interest,

Ethical approval

Not Applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rani, S., Pravija Raj, P.V. & Khedr, A.M. SDESA: secure cloud computing with gradient deep belief network and congruential advanced encryption. J Supercomput 80, 23147–23176 (2024). https://doi.org/10.1007/s11227-024-06322-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-06322-3

Keywords