Performance Enhancement in Cloud Based Network Using Polynomial Encryption and Deep Learning


  • Ajit Singh, Geeta Rani, Saya


Cloud Computing, Polynomial Encryption, Security, Confusion Matrix, Accuracy parameters, Deep Learning.


Present paper provides the conceptual framework for enhancement of performance in cloud -based network using the concepts of polynomial encryption and deep learning. The earlier techniques like RSA, AES and DES etc. used for security systems were slow and provided limited security. In order to enhance the performance and safety of cloud servers, polynomial encryption mechanism with the concept of deep learning has been developed by modifying the existing data encryption techniques to allow novel hybrid cryptography processes. This proposed versatile security mechanism is capable to deal with denial of service, brute force attack and man in middle attack and also capable to classify different type of attacks for the protection of cloud-based networks. The deep learning approach used to restrict invalid data transmission along with data encryption. Further, the mathematical calculations shows that the proposed mechanism provides 85% accuracy whereas the conventional mechanism having accuracy of 75%. Similarly, the value of other parameters like security, precision, recall value and F1-Score of proposed mechanism are better than existing conventional mechanism. Hence, system based on the proposed mechanism is having more efficient, flexible and scalable than the existing one.


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Z. Brakerski, “Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP,” Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 868–886, 2012. doi: 10.1007/978-3-642-32009-5_50.

T. Plantard, W. Susilo, and Z. Zhang, “Fully Homomorphic Encryption Using Hidden Ideal Lattice,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 12. Institute of Electrical and Electronics Engineers (IEEE), pp. 2127–2137, Dec. 2013. doi: 10.1109/tifs.2013.2287732.

G. Ye, C. Pan, X. Huang, and Q. Mei, “An efficient pixel-level chaotic image encryption algorithm,” Nonlinear Dynamics, vol. 94, no. 1. Springer Science and Business Media LLC, pp. 745–756, Jun. 05, 2018. doi: 10.1007/s11071-018-4391-y.

H. Diab, “An Efficient Chaotic Image Cryptosystem Based on Simultaneous Permutation and Diffusion Operations,” IEEE Access, vol. 6. Institute of Electrical and Electronics Engineers (IEEE), pp. 42227–42244, 2018. doi: 10.1109/access.2018.2858839.

A. Fu, S. Li, S. Yu, Y. Zhang, and Y. Sun, “Privacy-preserving composite modular exponentiation outsourcing with optimal checkability in single untrusted cloud server,” Journal of Network and Computer Applications, vol. 118. Elsevier BV, pp. 102–112, Sep. 2018. doi: 10.1016/j.jnca.2018.06.003.

M. Wazid, S. Zeadally, and A. K. Das, “Mobile Banking: Evolution and Threats: Malware Threats and Security Solutions,” IEEE Consumer Electronics Magazine, vol. 8, no. 2. Institute of Electrical and Electronics Engineers (IEEE), pp. 56–60, Mar. 2019. doi: 10.1109/mce.2018.2881291.

L. Huang, S. Cai, X. Xiong, and M. Xiao, “On symmetric color image encryption system with permutation-diffusion simultaneous operation,” Optics and Lasers in Engineering, vol. 115. Elsevier BV, pp. 7–20, Apr. 2019. doi: 10.1016/j.optlaseng.2018.11.015.

N. Hasan and A. Farhan, “Security Improve in ZigBee Protocol Based on RSA Public Algorithm in WSN,” Engineering and Technology Journal, vol. 37, no. 3B. University of Technology, pp. 67–73, Oct. 25, 2019. doi: 10.30684/etj.37.3b.1.

W. Liu, S. Fan, A. Khalid, C. Rafferty, and M. O’Neill, “Optimized Schoolbook Polynomial Multiplication for Compact Lattice-Based Cryptography on FPGA,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 10. Institute of Electrical and Electronics Engineers (IEEE), pp. 2459–2463, Oct. 2019. doi: 10.1109/tvlsi.2019.2922999.

D. Harvey and J. van der Hoeven, “Faster polynomial multiplication over finite fields using cyclotomic coefficient rings,” Journal of Complexity, vol. 54. Elsevier BV, p. 101404, Oct. 2019. doi: 10.1016/j.jco.2019.03.004.

L. Liu, Y. Lei, and D. Wang, “A Fast Chaotic Image Encryption Scheme With Simultaneous Permutation-Diffusion Operation,” IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers (IEEE), pp. 27361–27374, 2020. doi: 10.1109/access.2020.2971759.

Koppanati, Ramakrishna & Kumar, Krishan. P-MEC: “Polynomial Congruence-Based Multimedia Encryption Technique Over Cloud”. IEEE Consumer Electronics Magazine. PP. 1-1. 10.1109/MCE.2020.3003127.(2020).

R. K. Salih and M. Sh. Yousif, “Hybrid encryption using playfair and RSA cryptosystems,” IJNAA, vol. 12, no. 2, Jul. 2021, doi: 10.22075/ijnaa.2021.5379.

N. N. H. Adenan, M. R. Kamel Ariffin, S. H. Sapar, A. H. Abd Ghafar, and M. A. Asbullah, “New Jochemsz–May Cryptanalytic Bound for RSA System Utilizing Common Modulus N = p2q,” Mathematics, vol. 9, no. 4. MDPI AG, p. 340, Feb. 08, 2021. doi: 10.3390/math9040340.

M. Song, Y. Sang, Y. Zeng, and S. Luo, “Blockchain-Based Secure Outsourcing of Polynomial Multiplication and Its Application in Fully Homomorphic Encryption,” Security and Communication Networks, vol. 2021. Hindawi Limited, pp. 1–14, Jun. 24, 2021. doi: 10.1155/2021/9962575.

K. Limniotis,Cryptography as the “Means to Protect Fundamental Human Rights. Cryptography” 5(4), 34; ,2021.

J.-P. Thiers and J. Freudenberger, “Code-Based Cryptography With Generalized Concatenated Codes for Restricted Error Values,” IEEE Open Journal of the Communications Society, vol. 3. Institute of Electrical and Electronics Engineers (IEEE), pp. 1528–1539, 2022. doi: 10.1109/ojcoms.2022.3206395.

Chong, B.; Salam, I. Investigating “Deep Learning Approaches on the Security Analysis of Cryptographic Algorithms”. Cryptography 5(4), 30;, 2021.

El-Attar, N.E.; El-Morshedy, D.S.; Awad, W.A. “A New Hybrid Automated Security Framework to Cloud Storage System”. Cryptography 2021, 5,37. ,2021.

Raghad K. Saliha. “Optimizing RSA cryptosystem using Hermite polynomials”. Int. J. Nonlinear Anal. Appl. 13 1, 955-961 ISSN: 2008-6822 (electronic),(2022)

C. D. Reddy, L. Lopez, D. Ouyang, J. Y. Zou, and B. He, “Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients,” Journal of the American Society of Echocardiography, vol. 36, no. 5. Elsevier BV, pp. 482–489, May 2023. doi: 10.1016/j.echo.2023.01.015.




How to Cite

Ajit Singh. (2024). Performance Enhancement in Cloud Based Network Using Polynomial Encryption and Deep Learning . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3698 –. Retrieved from



Research Article