Cybersecurity in  Cloud Systems: Emerging Threats and Advanced Security Solutions

Hosinisharif, seyedamin; karimi, atefeh

[Home ] [Archive]

[ فارسی ]

Main Menu

Home

Journal Information

Articles archive

For Authors

For Reviewers

Registration

Contact us

Site Facilities

Search in website

Receive site information

Volume 4, Issue 9 (12-2025)

3 2025, 4(9): 9-15

Back to browse issues page

Cybersecurity in Cloud Systems: Emerging Threats and Advanced Security Solutions

Seyedamin Hosinisharif

, Atefeh Karimi

Abstract: (61 Views)

Cybersecurity in cloud systems stands as one of the primary challenges in the digital age. With the growing adoption of cloud technologies, cyber threats have increased significantly. This paper examines emerging threats in the cloud environment, including ransomware attacks, DDoS attacks, and AI‑based attacks. Moreover, advanced security solutions such as the use of blockchain, zero‑trust architectures, and homomorphic encryption are analyzed. The findings of this research indicate that technologies such as blockchain, by offering transparency and data immutability, are effective in countering ransomware attacks. Deep learning models are also capable of detecting complex attacks like DDoS and zero‑day attacks with high accuracy. Zero‑trust architectures significantly enhance cloud security by reducing the attack surface and enabling continuous monitoring. On the other hand, homomorphic encryption preserves data privacy in cloud environments by allowing computations on encrypted data. Despite numerous advantages, implementing these solutions faces challenges such as technical complexity and computational overhead. By comparing different security methods based on criteria such as accuracy, computational complexity, and training data requirements, this paper proposes optimized solutions for improving the security of cloud systems.

Keywords: Cybersecurity, Cloud Systems, Blockchain, Deep Learning, Zero Trust Architecture, Homomorphic Encryption, DDoS Attacks, Insider Threats.

Full-Text [PDF 429 kb] (59 Downloads)

Type of Study: case report | Subject: Special
Received: 2025/05/12 | Accepted: 2025/12/1 | Published: 2025/12/1

References

1. Park, J. H., & Park, J. H. (2017). Blockchain security in cloud computing: Use cases, challenges, and solutions. Symmetry, 9(8), 164.

2. Chen, F. (2024). Enhancing cloud computing security with blockchain: A hybrid approach to data privacy and integrity. International Journal of Cloud Computing and Services Science, 13(1), 45-58.

3. A. Ezzati and M. M. ShirMohammadi, "A novel approach to increase the lifetime and security of wireless sensor networks using a combination of particle swarm optimization algorithms and K-Mean combined with an …," Intelligent Knowledge Exploration and Processing, vol. 5, no. 16, p. e226723, May 2025.

4. M. M. Shirmohammadi and S. M. HosseiniKia, "Efficient Routing and Lifetime Enhancement in Wireless Sensor Network Performance Using Artificial Bee Colony Algorithm and Sleep-Wake Algorithm," Journal of Information Technology and Network Security, vol. 1, no. 3, pp. 1-10, 2024

5. S. Z. Majidian and M. M. Shirmohammadi, “Clustering and Routing in Wireless Sensor Networks Using Multi-Objective Cuckoo Search and Game Theory,” *Electronic and Cyber Defense*, vol. 10, no. 3, pp. 11-20, Dec. 2022.

6. M. M. Shirmohammadi, M. Chahardoli, Wireless Sensor Networks. Hamadan: Islamic Azad University, Hamadan Branch, 2012. ISBN: 978-964-543-103-5

7. Modi, C., Patel, D., Borisaniya, B., Patel, A., & Rajarajan, M. (2013). A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications, 36(1), 42-57

8. Chen, L., Jordan, S., Liu, Y. K., Moody, D., Peralta, R., Perlner, R.,... & Smith-Tone, D. (2016). Report on Post-Quantum Cryptography (NISTIR 8105). Gaithersburg, MD: National Institute of Standards and Technology.

9. H. Yari, M. Esmaeilpour, M. M. Shirmohammadi, "Improved Low-Power Coverage with Variable Radius in Voronoi-Based Wireless Sensor Networks: Firefly Swarm Optimization-K-Means Algorithm," Intell. Inf. Syst. J., vol. 3, no. 6, pp. 11–20, Jan. 2025.

10. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153–1176.

11. M. M. Shirmohammadi and M. Chahhardi, “ESEP: A Stable Cluster Head Selection Protocol in Heterogeneous Wireless Sensor Networks with Sleep and Wake Modes,” in *Proceedings of the Computer, Electrical and Information Technology Conference*, Islamic Azad University, Hamadan Branch, Hamadan, vol. 1, 2007

12. Peikert, C. (2016). A decade of lattice cryptography. Foundations and Trends® in Theoretical Computer Science, 10(4), 283–424.

13. Tanam, A., & Raja, G. (2024). Enhancing cloud security through blockchain: A data integrity and trust approach. International Journal of Safety and Security Engineering, 14(5), 513–522.

14. Selesi-Aina, O. (2024). Blockchain-enhanced cloud security frameworks: Addressing human-network vulnerabilities in public and private sector systems. Journal of Engineering Research and Reports, 24(6), 1–14.

15. Sommer, P., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In Proceedings of the 2010 IEEE Symposium on Security and Privacy (pp. 305–316). Oakland, CA: IEEE.

Send email to the article author

Add your comments about this article

Mendeley

Zotero

RefWorks

Hosinisharif S, karimi A. Cybersecurity in Cloud Systems: Emerging Threats and Advanced Security Solutions. 3 2025; 4 (9) :9-15
URL: http://jiis.iauh.ac.ir/article-1-39-en.html

Rights and permissions
	This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Volume 4, Issue 9 (12-2025)

Back to browse issues page

Persian site map - English site map - Created in 0.05 seconds with 37 queries by YEKTAWEB 4735