Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography

Year : 2024 | Volume :02 | Issue : 01 | Page : 1-10
By

Padma Bhogaraju,

D. Deepthi,

  1. Associate Professor, Gayatri Vidya Parishad College for Degree and PG Courses (Autonomous), Visakhapatnam, Andhra Pradesh, India
  2. Assistant Professor, Rayapati Venkata Rangarao and Jagarlamudi Chandramouli (RVR&JC) College of Engineering (Autonomous), Guntur, Andhra Pradesh, India

Abstract

In modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns and behaviours, machine learning algorithms enable proactive threat detection. Machine learning models can distinguish normal network activity from malicious incursions by analysing large datasets, improving early threat detection and response. By using cryptographic protocols like homomorphic encryption and multi-party computation, private clouds can protect sensitive data while maintaining computational capabilities. This study examines how machine learning and cryptography can strengthen private cloud security against sophisticated cyber-attacks. Industry practitioners, academic scholars, and cyber security professionals seeking innovative private cloud asset protection strategies will benefit from the findings. As organizations move to private cloud infrastructures for flexibility and scalability, advanced security paradigms are needed to adapt to changing threats. This study suggests that combining cutting-edge technologies like machine learning and cryptography can enhance private cloud defences, both theoretically and practically.

Keywords: Homomorphic encryption, multi-party computations, machine learning models, cryptography and private cloud Security

[This article belongs to International Journal of Advanced Control and System Engineering(ijacse)]

How to cite this article: Padma Bhogaraju, D. Deepthi. Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography. International Journal of Advanced Control and System Engineering. 2024; 02(01):1-10.
How to cite this URL: Padma Bhogaraju, D. Deepthi. Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography. International Journal of Advanced Control and System Engineering. 2024; 02(01):1-10. Available from: https://journals.stmjournals.com/ijacse/article=2024/view=157574

References

  1. Gu L, Wang L, Ota K, Dong M, Cao Z, Yang New public key cryptosystems based on non-abelian factorization problems. Secur Commun Netw. 2013; 6(7): 912–922.
  2. Au MH, Liang K, Liu JK, Lu R, Ning J. Privacy-preserving personal data operation on mobile cloud—Chances and challenges over advanced persistent threat. Future Gener Comput Syst. 2018 Feb 1; 79: 337–
  3. Chou D, Jiang M. A survey on data-driven network intrusion detection. ACM Comput Surv (CSUR). 2021 Oct 7; 54(9): 1–
  4. Ouyang L, Yuan Y, Wang FY. Learning markets: An AI collaboration framework based on blockchain and smart contracts. IEEE Internet Things J. 2020 Oct 21; 9(16): 14273–
  5. Mahmood ALGS. Data Security Protection in Cloud Computing by using Encryption. Kirkuk Univ Journal/Scientific Stud. 2017; 12(4): 849–1992.
  6. Gentry C, et al. Fully homomorphic encryption using ideal lattices. In: Stoc’09: Proceedings of the forty-first annual ACM symposium on Theory of computing. 2009; 9: 169–178.
  7. Al-Attab BS, Fadewar HS. Hybrid data encryption technique for data security in cloud computing. Sinhgad Inst Manag Comput Appl. 2018; 221–224.
  8. Rivest RL, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems. Commun ACM. 1978; 21(2): 120–126.
  9. Uçtu G, Alkan M, Doğru İA, Dörterler M. A suggested testbed to evaluate multicast network and threat prevention performance of Next Generation Firewalls. Futur Gener Comput Syst. 2021; 124: 56–67. doi: 10.1016/j.future.2021.05.013.
  10. Vora U, Mahato J, Dasgupta H, Kumar A, Ghosh SK. Machine Learning–Based Security in Cloud Database—A Survey. In: IEEE Mach Learn Tech Anal Cloud Secur. 2021; 239–269. doi: 10.1002/9781119764113.ch12.
  11. Gu L, Zheng S. Conjugacy systems based on nonabelian factorization problems and their applications in cryptography. J Appl Math. 2014; 2(2014): 1–10.
  12. Kolli R, Mile S, Shetty S, SJB, CBM. Improved Data Security Protection Mechanism for Cloud Storage using Two Factors.Int J Adv Res Comput Commun Eng (Ijarcce). 2017; 6(5): 24–29. doi:10.17148/ijarcce.2017.6505.
  13. Pandeeswari N, Kumar G. Anomaly detection system in cloud environment using fuzzy clustering based ANN. Mob Networks Appl. 2016; 21(3): 494–505. doi: 10.1007/s11036-015-0644-x.

 

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Volume 02
Issue 01
Received May 21, 2024
Accepted June 25, 2024
Published July 22, 2024
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