The Role of AI in Enhancing Cloud Security: A Comprehensive Analysis of Its Impact on the Indian IT Industry

Authors

  • Syed Minhaj UI Hassan, Meena Chaudhary

Keywords:

Cloud Computing, Artificial Intelligence, Machine Learning, Internet of Things, Tesla, Algorithms, Linear Regression, Logistic Regression, Automated ML, Data Management, Synthetic Data, Analytics Platform

Abstract

A rapidly expanding area of study, AI in cloud computing aims to provide smart solutions for various sectors. Businesses may use AI cloud computing's Machine Learning and Statistical capabilities to build dynamic apps with the power to execute complex computations. Artificial intelligence (AI) in the cloud is all about creating smart apps, assisting businesses with Big Data, using algorithms to make apps more powerful, and predicting and forecasting growth, which are huge boons to a company's bottom line and longevity. The article delves into the history of AI in cloud computing, how it has changed over time, the advantages it offers to big and small businesses, current market trends, examples of its application, and projections for the future.

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References

Subramanian, E. K., & Tamilselvan, L. (2019). A focus on the future cloud: Machine learning-based cloud security. Service Oriented Computing and Applications, 13(3), 237–249. https://doi.org/10.1007/s11761-019-00270-0

Fernandes, D. A. B., Soares, L. F. B., Gomes, J. V., Freire, M. M., & Inácio, P. R. M. (2014). Security issues in cloud environments: A survey. International Journal of Information Security, 13(2), 113–170. https://doi.org/10.1007/s10207-013-0208-7

Muralidhara, P. (2017). The evolution of cloud computing security: Addressing emerging threats. International Journal of Computer Science and Technology, 1(4), 1–33.

Achar, S. (2022). Adopting artificial intelligence and deep learning techniques in cloud computing for operational efficiency. International Journal of Information and Communication Engineering, 16(12), 567–572.

Nassif, A. B., Abu Talib, M., Nasir, Q., Albadani, H., & Dakalbab, F. M. (2021). Machine learning for cloud security: A systematic review. IEEE Access: Practical Innovations, Open Solutions, 9, 20717–20735. https://doi.org/10.1109/ACCESS.2021.3054129

Khorshed, M. T. (2011). Trust issues create threats for cyber-attacks in cloud computing. In 2011 IEEE 17th International Conference on Parallel and Distributed Systems (pp. 900-905). IEEE. https://doi.org/10.1109/ICPADS.2011.156

Kumar, R., Lal, S. P., & Sharma, A. (2016). Detecting denial of service attacks in the cloud. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing (pp. 309–316). IEEE. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.70

Moreno-Vozmediano, R., Montero, R. S., Huedo, E., & Llorente, I. M. (2019). Efficient resource provisioning for elastic cloud services based on machine learning techniques. Journal of Cloud Computing (Heidelberg, Germany), 8(1), 1–18. https://doi.org/10.1186/s13677-019-0128-9

Dave, D., Meruliya, N., Gajjar, T. D., Ghoda, G. T., Parekh, D. H., & Sridaran, R. (2018). Cloud security issues and challenges. In Big Data Analytics: Proceedings of CSI 2015 (pp. 499-514). Springer Singapore. https://doi.org/10.1007/978-981-10-6620-7_48

Nenvani, G., & Gupta, H. (2016). A survey on attack detection on cloud using supervised learning techniques. In 2016 Symposium on Colossal Data Analysis and Networking (CDAN) (pp. 1-5). IEEE. https://doi.org/10.1109/CDAN.2016.7570872

Hesamifard, E., Takabi, H., Ghasemi, M., & Jones, C. (2017). Privacy-preserving machine learning in the cloud. In Proceedings of the 2017 on Cloud Computing Security Workshop (pp. 39–43). ACM. https://doi.org/10.1145/3140649.3140655

He, Z., Zhang, T., & Lee, R. B. (2017). Machine learning-based DDoS attack detection from the source side in the cloud. In 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 114-120). IEEE. https://doi.org/10.1109/CSCloud.2017.58

Butt, U. A., Mehmood, M., Syed, B. H. S., Amin, R., Shaukat, M. W., Raza, S. M., Suh, D. Y., & Piran, M. J. (2020). A review of machine learning algorithms for cloud computing security. Electronics (Basel), 9(9), 1379. https://doi.org/10.3390/electronics9091379

Salman, T., Bhamare, D., Erbad, A., Jain, R., & Samaka, M. (2017). Machine learning for anomaly detection and categorization in multi-cloud environments. In 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 97-103). IEEE. https://doi.org/10.1109/CSCloud.2017.15

Malaiyappan, J. N. A., Karamthulla, M. J., & Tadimarri, A. (2023). Towards autonomous infrastructure management: A survey of AI-driven approaches in platform engineering. Journal of Knowledge Learning and Science Technology, 2(2), 303-314.

Talati, D. (2023). AI in the healthcare domain. Journal of Knowledge Learning and Science Technology, 2(3), 256–262.

Talati, D. (2023). Telemedicine and AI in remote patient monitoring. Journal of Knowledge Learning and Science Technology, 2(3), 254–255.

Talati, D. (2024). Virtual health assistance–AI-based. Authorea Preprints.

Talati, D. (2023). Artificial intelligence (AI) in mental health diagnosis and treatment. Journal of Knowledge Learning and Science Technology, 2(3), 251–253.

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Published

09.07.2024

How to Cite

Syed Minhaj UI Hassan. (2024). The Role of AI in Enhancing Cloud Security: A Comprehensive Analysis of Its Impact on the Indian IT Industry. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 1600 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/6709

Issue

Vol. 12 No. 22s (2024)

Section

Research Article

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