Leveraging Quantum Computing for Enhanced Cryptographic Protocols in Cloud Security

Main Article Content

Swamy T
Sunil Vijaya Kumar Gaddam

Abstract

Quantum computing has emerged as a transformative technology with the potential to revolutionize various domains, including cryptography. This research paper addresses the pressing issue of enhancing cryptographic protocols in cloud security through the integration of quantum computing. The main objective of this study is to develop and evaluate novel cryptographic protocols that leverage the computational power of quantum systems to enhance security measures in cloud environments.To achieve this objective, we employed a hybrid methodology that combines classical cryptographic techniques with quantum algorithms. Specifically, we designed a framework incorporating quantum key distribution (QKD) and post-quantum cryptographic (PQC) algorithms to ensure robust security against quantum attacks. The framework was implemented and tested in a simulated cloud environment to assess its effectiveness and performance.The results indicate that the proposed quantum-enhanced cryptographic protocols significantly improve the security and resilience of cloud systems. Our findings demonstrate that the integration of QKD and PQC provides a higher level of security compared to traditional cryptographic methods, effectively mitigating potential threats posed by quantum computers. Additionally, the performance analysis shows that the proposed protocols are computationally feasible and can be integrated into existing cloud infrastructures with minimal overhead.In conclusion, this research highlights the potential of quantum computing to address critical security challenges in cloud computing. The proposed quantum-enhanced cryptographic protocols offer a promising solution for protecting sensitive data in cloud environments against emerging quantum threats. Future work will focus on refining these protocols and exploring their practical applications in real-world cloud systems.


.

Article Details

How to Cite
[1]
Swamy T and Sunil Vijaya Kumar Gaddam, “Leveraging Quantum Computing for Enhanced Cryptographic Protocols in Cloud Security”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 5, pp. 1–8, Jun. 2024.
Section
Research Articles

References

. Namita M. Butale, & Dattatraya.V.Kodavade. (2018). Survey Paper on Detection of Unhealthy Region of Plant Leaves Using Image Processing and Soft Computing Techniques. International Journal of Computer Engineering in Research Trends, 5(12), 232–235.

. Sangeetha, R., Logeshwaran, J., Rocher, J., & Llo-ret, J. (2023). An improved agro deep learning model for detection of Panama wilts disease in banana leaves. AgriEngineering, 5(2), 660-679.

. Singh, R., & Athisayamani, S. (2020). Banana leaf diseased image classification using novel HEAP auto encoder (HAE) deep learning. Multimedia Tools and Applications, 79(41), 30601-30613.

. Krishnan, V. G., Deepa, J. R. V. P., Rao, P. V., Divya, V., & Kaviarasan, S. (2022). An automated segmentation and classification model for banana leaf disease detection. Journal of Applied Biology and Biotechnology, 10(1), 213-220.

. Vipinadas, M. J., & Thamizharasi, A. (2016). Ba-nana leaf disease identification tech-nique. International Journal of Advanced Engi-neering Research and Science, 3(6), 236756.

. Hu, W., Ma, X., Liu, G., & Li, Y. (2020). Identifica-tion of grape leaf diseases based on deep convolu-tional neural networks. Neurocomputing, 383, 308-321. https://doi.org/10.1016/j.neucom.2019.11.007

. Ahmed, S., et al. (2021). Ethical considerations in deploying deep learning models for agricultural applications. Journal of Agricultural Ethics, 35(2), 234-250.

. Chen, J., et al. (2021). Attention mechanisms in convolutional neural networks for agricultural disease prediction. Computers and Electronics in Agriculture, 178, 105761.

. Jones, A., & Smith, B. (2018). Satellite imagery for crop disease prediction. Remote Sensing of Envi-ronment, 215, 202-214.

. Kim, Y., et al. (2019). Drone-based imaging for high-accuracy crop disease detection. Precision Agriculture, 20(3), 123-136.

. Li, X., et al. (2021). Transfer learning in deep learning models for agricultural applications. IEEE Access, 9, 12345-12356.

. Patil, R., et al. (2022). Hybrid models for crop dis-ease prediction. Agricultural Systems, 195, 103297.

. Picon, A., et al. (2019). Banana leaf disease classi-fication using CNNs. Computers and Electronics in Agriculture, 156, 134-141.

. Ramesh, M., et al. (2019). Mobile applications for real-time crop disease diagnosis. Journal of Agri-cultural Informatics, 10(1), 45-56.

. Singh, P., et al. (2020). Sustainable farming prac-tices supported by deep learning. Agriculture, 10(11), 522.

. Thompson, H., & Garcia, M. (2020). Data privacy concerns in agricultural technology. Technology in Society, 63, 101391.

. Arman, S. E., Bhuiyan, M. A. B., Abdullah, H. M., Islam, S., Chowdhury, T. T., & Hossain, M. A. (2023). BananaLSD: A banana leaf images da-taset for classification of banana leaf diseases us-ing machine learning. Data in Brief, 50, 109608.

. Zhang, H., et al. (2020). Early disease detection in banana plants using deep learning. IEEE Transactions on Automation Science and Engi-neering, 17(3), 1195-1206.

. Kalyan, L.P., Nagaraju, G., Reddy, R.K., Mulkala-palli, S. (2022). Identification of Face Mask De-tection Using Convolutional Neural Networks. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M., Purushothama, B.R. (eds) Interna-tional Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Elec-trical Engineering, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-16-8546-0_25

. T Sunil Kumar, Gujjeti Sridhar, D Manju, P Sub-hash, Gujjeti Nagaraju. "Breast Cancer Classifi-cation and Predicting Class Labels Using Res-Net50." Journal of Electrical Systems 19.4 (2024) 270-278. https://doi.org/10.52783/jes.638.

. G. Nagaraju, Rajiv Kumar Nath, P. Chinniah, K. Balasubramanian, S. Kirubakaran, Balasub-bareddy Mallala. (2024). A Comparative analysis of Advanced Machine Learning Techniques for Enhancing Brain Tumor Detection. Journal of Electrical Systems. 20. 901-909. https://doi.org/10.52783/jes.1687

. Vipinadas, M. J., & Thamizharasi, A. (2016). Ba-nana leaf disease identification tech-nique. International Journal of Advanced Engi-neering Research and Science, 3(6), 236756.

. Aruraj, A., Alex, A., Subathra, M. S. P., Sairamya, N. J., George, S. T., & Ewards, S. V. (2019, March). Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In 2019 2nd Interna-tional Conference on Signal Processing and Communication (ICSPC) (pp. 231-235). IEEE.