Real-time crypt analysis and defense mechanisms in cloud computing services

Moulick, Jayita; Phursule, Rajesh; Kale, Archana P.; Rana, Varinder Singh; Goswami, Shubham; Karwande, Vijay S.

doi:10.47974/JDMSC-2175

Open Access Research Article

Real-time crypt analysis and defense mechanisms in cloud computing services

^*Jayita MoulickCorresponding authorjayita.moulick@symlaw.ac.inSymbiosis Law School Pune (SLS-P)Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR), Symbiosis International (Deemed University)Pune, Maharashtra, 411014, IndiaView full profile → , Rajesh Phursulerphursule@gmail.comDepartment of Information TechnologyPimpri Chinchwad College of EngineeringPune, Maharashtra, 411044, IndiaView full profile → , Archana P. Kalearchana.kale@mescoepune.orgDepartment of Computer EngineeringModern Education Society’s College of EngineeringPune, Maharashtra, 411005, IndiaView full profile → , Varinder Singh Ranar.varinder@cu.ac.aeCollege of BusinessCity University AjmanAjman, 18484, U.A.E.View full profile → , Shubham Goswamishubham.goswami@niu.edu.inDepartment of Computer Science & EngineeringNoida International UniversityUttar Pradesh, 203201, IndiaView full profile → , Vijay S. Karwandehodcse@eescoet.orgDepartment of Computer Science and EngineeringEverest College of Engineering and TechnologyAurangabad, Maharashtra, 431119, IndiaView full profile →

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pp. 1753–1761Vol. 28Issue 5-AAugust 2025DOI: 10.47974/JDMSC-2175 Crossmark XML

Received:: 06 Nov 2024
Published Online:: 30 Aug 2025
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-2175
Pages:: 1753–1761

Abstract

The expanding dependence on cloud computing administrations has required progressed components for real-time tomb examination and defense to defend delicate information. This paper proposes a novel Various leveled Unified Learning (HFL) system outlined to improve versatility and productivity in dispersed inconsistency discovery inside cloud situations. The HFL strategy structures the learning handle into numerous layers—local, territorial, and global—facilitating productive show conglomeration and diminishing communication overhead. By leveraging territorial conglomeration some time recently worldwide integration, the proposed HFL system optimizes asset utilization and moves forward versatility, pleasing a better number of clients with decreased idleness. In addition, the HFL approach coordinating real-time irregularity discovery and reaction instruments, guaranteeing that rising dangers are recognized and relieved expeditiously. Comparative examination with existing strategies such as Combined Averaging (FedAvg), Personalized Unified Learning (PerFedAvg), and Unified Exchange Learning (FedTL) illustrates that the HFL system offers predominant show exactness, diminished communication overhead, and speedier joining times. Also, the proposed strategy improves information security and exactness in identifying irregularities. This investigate underscores the potential of HFL as a vigorous arrangement for real-time tomb investigation and defense, clearing the way for more secure and adaptable cloud computing administrations.

Keywords

Hierarchical federated learning (HFL)Real-time crypt analysis Cloud computing security Anomaly detection Scalability

Subject Classifications

68M12

References

[1] J. K. Lee, S. Y. Moon, and J. H. Park, “CloudRPS: a cloud analysis based enhanced ransomware prevention system,” J. Supercomput., vol. 73, no. 7, pp. 3065–3084 (2017), doi: 10.1007/s11227-016-1825-5.
[2] M. Bazm, M. Lacoste, M. Südholt, and J. Menaud, “Side Channels in the Cloud : Isolation Challenges, Attacks, and Countermeasures,” (2017).
[3] A. Ait, N. Ammari, A. Abou, A. Ait, and M. De, “New mechanism for Cloud Computing Storage Security,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 7, pp. 526–539 (2016), doi: 10.14569/ijacsa.2016.070773.
[4] R. Montasari, A. Daneshkhah, H. Jahankhani, and A. Hosseinian-Far, “Cloud Computing Security: Hardware-Based Attacks and Countermeasures BT - Digital Forensic Investigation of Internet of Things (IoT) Devices,” R. Montasari, H. Jahankhani, R. Hill, and S. Parkinson, Eds. Cham: Springer International Publishing, pp. 155–167 (2021).
[5] M. Tahir, M. Sardaraz, Z. Mehmood, and S. Muhammad, “CryptoGA: a cryptosystem based on genetic algorithm for cloud data security,” Cluster Comput., vol. 24, no. 2, pp. 739–752 (2021), doi: 10.1007/s10586-020-03157-4.
[6] F. Thabit, S. Alhomdy, and S. Jagtap, “Security analysis and performance evaluation of a new lightweight cryptographic algorithm for cloud computing,” Glob. Transitions Proc., vol. 2, no. 1, pp. 100–110 (2021), doi: https://doi.org/10.1016/j.gltp.2021.01.014.
[7] S. Lee, N. Jho, D. Chung, Y. Kang, and M. Kim, “Rcryptect: Real-time detection of cryptographic function in the user-space filesystem,” Comput. Secur., vol. 112, p. 102512 (2022), doi: https://doi.org/10.1016/j.cose.2021.102512.
[8] S. Ray, K. N. Mishra, and S. Dutta, “Big Data Security Issues from the Perspective of IoT and Cloud Computing: A Review,” Recent Adv. Comput. Sci. Commun., vol. 14, no. 7, pp. 2057–2078 (2020), doi: 10.2174/2666255813666200224092717.
[9] O. Can, F. Thabit, A. O. Aljahdali, S. Al-Homdy, and H. A. Alkhzaimi, “A Comprehensive Literature of Genetics Cryptographic Algorithms for Data Security in Cloud Computing,” Cybern. Syst., pp. 1–35, doi: 10.1080/01969722.2023.2175117.
[10] Q. Mei, H. Xiong, Y.-C. Chen, and C.-M. Chen, “Blockchain-Enabled Privacy-Preserving Authentication Mechanism for Transportation CPS With Cloud-Edge Computing,” IEEE Trans. Eng. Manag., vol. 71, pp. 12463–12474 (2024), doi: 10.1109/TEM.2022.3159311.
[11] D. Elangovan and R. Muthiya, “Design and implementation of crypt analysis of cloud data intrusion management system,” Int. Arab J. Inf. Technol., vol. 17, no. 6, pp. 895–905 (2020), doi: 10.34028/iajit/17/6/8.
[12] F. Ayankoya and B. Ohwo, “Brute-Force Attack Prevention in Cloud Computing Using One-Time Password and Cryptographic Hash Function,” Int. J. Comput. Sci. Inf. Secur., vol. 17, no. 2, pp. 7–19 (2019), [Online]. Available: https://www.academia.edu/38523734/Brute-Force_Attack_Prevention_in_Cloud_Computing_Using_One-Time_Password_and_Cryptographic_Hash_Function.
[13] M. Sohal and S. Sharma, “Comparative Analysis of Different Cryptographic Mechanisms of Data Security and Privacy in Cloud Environment,” Int. J. Comput. Sci. Eng., vol. 6, no. 12, pp. 12–24 (2018), doi: 10.26438/ijcse/v6i12.1224.
[14] M. U. Sana, Z. Li, F. Javaid, H. B. Liaqat, and M. U. Ali, “Enhanced Security in Cloud Computing Using Neural Network and Encryption,” IEEE Access, vol. 9, pp. 145785–145799 (2021), doi: 10.1109/ACCESS.2021.3122938.
[15] D. Goyal, A. Kumar, Y. Gandhi, and V. Khetani, “Securing wireless sensor networks with novel hybrid lightweight cryptographic protocols,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 27, no. 2-B, pp. 703–714 (2024), doi: 10.47974/JDMSC-1921.

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