AI-Powered Cloud Security: A Study on the Integration of Artificial Intelligence and Machine Learning for Improved Threat Detection and Prevention

Anandharaj N.

Authors

Anandharaj N. Author

Keywords:

AI-powered cloud security, Artificial intelligence, Machine learning, Threat detection, Threat prevention, Cloud security, Cybersecurity, Predictive analytics

Abstract

AI-powered cloud security has emerged as a transformative approach to address the growing complexity and sophistication of cyber threats in the cloud computing era. This study explores the integration of artificial intelligence (AI) and machine learning (ML) techniques to enhance threat detection, prevention, and response capabilities within cloud-based security solutions.

The paper delves into the key drivers behind the adoption of AI-powered cloud security, including the exponential growth of cloud-based data and applications, the increasing prevalence of advanced persistent threats, and the need for real-time, adaptive security measures. It examines how AI and ML algorithms can be leveraged to analyze vast amounts of security-related data, identify anomalies, and detect emerging threats with greater accuracy and speed than traditional security approaches. The study also investigates the various AI-powered security capabilities, such as predictive analytics, automated incident response, and self-healing systems, that can be integrated into cloud security platforms. It explores the challenges and considerations associated with the implementation of AI-powered cloud security, including data privacy, model interpretability, and the integration of AI with existing security infrastructure. The paper presents case studies and real-world examples that demonstrate the tangible benefits of AI-powered cloud security, such as improved threat detection rates, reduced incident response times, and enhanced overall security posture. The findings of this study provide valuable insights for cloud service providers, security professionals, and decision-makers seeking to leverage the power of AI and ML to strengthen their cloud security strategies.

References

Mohammed Rizvi. Enhancing cybersecurity: The power of artificial intelligence in threat detection and prevention. International Journal of Advanced Engineering Research and Science (IJAERS). Vol-10, Issue-5; May, 2023.

Salih, S. T. Zeebaree, S. Ameen, A. Alkhyyat and H. M. Shukur, "A Survey on the Role of Artificial Intelligence, Machine Learning and Deep Learning for Cybersecurity Attack Detection," 2021 7th International Engineering Conference “Research & Innovation amid Global Pandemic" (IEC), Erbil, Iraq, 2021, pp. 61-66.

Sure, T. A. R. (2023). Image Processing Using Artificial Intelligence in iOS. Journal of Computer Science Engineering and Software Testing, 9(3), 10-15.

Ahmed. N.,(2021). Machine Learning and Big Data Analytics for Cybersecurity Threat Detection: A Holistic Review of Techniques and Case Studies. SSRAML Sage Science, 1(1), 51–63.

Sakthiswaran Rangaraju. AI SENTRY: REINVENTING CYBERSECURITY THROUGH INTELLIGENT THREAT DETECTION. EPH - International Journal Of Science And Engineering. Volume 09 Issue 03 November 2023.

Ara, A., &Mifa, A. F. (2024). INTEGRATING ARTIFICIAL INTELLIGENCE AND BIG DATA IN MOBILE HEALTH: A SYSTEMATIC REVIEW OF INNOVATIONS AND CHALLENGES IN HEALTHCARE SYSTEMS. Global Mainstream Journal of Business, Economics, Development & Project Management, 3(01), 01-16.

Sure, T. A. R. (2023). The role of mobile applications and AI in continuous glucose monitoring: A comprehensive review of key scientific contributions. International Journal of Artificial Intelligence in Medicine (IJAIMED), 1(1), 9-13.

Padmanaban, H. (2023). Navigating the intricacies of regulations: Leveraging AI/ML for Accurate Reporting. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386. (online), 2(3), 401-412.

Camacho, N. G. (2024). The Role of AI in Cybersecurity: Addressing Threats in the Digital Age. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 3(1), 143-154.

Sure, T. A. R. (2023). ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN iOS. International Journal of Artificial Intelligence & Machine Learning (IJAIML), 2(1), 82-87.

Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. Journal of Network and Computer Applications 34(1), 1–11 (2011).

Huang, C., Ali, W., Li, F., Lin, C.W.: AI-based intrusion detection in the cloud environment: A review. IEEE Access 8, 86103–86117 (2020).

Moustafa, N., Creech, G., Slay, J.: Big data analytics for intrusion detection system: Statistical decision-making using finite dirichlet mixture models. In: Future Generation Computer Systems 82, 15–25 (2018).

Sood, K., Sarje, A.K., Singh, K.: A novel protocol for secure communication in cloud computing environment. In: Journal of Network and Computer Applications 34(1), 166–175 (2011).

Bhattacharya, P., Jha, R., Mandal, A.K., Dutta, A.: A survey on cloud-based security and privacy issues in smart grid. In: Journal of Cloud Computing 9(1), 1–29 (2020).

Khan, M.A., Al-Yasiri, A.: Identifying cloud security threats to strengthen cloud computing adoption framework. Procedia Computer Science 94, 485–490 (2016).

Sure, T. A. R. (2023). Using Apple's ResearchKit and CareKit Frameworks for Explainable Artificial Intelligence Healthcare. Journal of Big Data Technology and Business Analytics, 2(3), 15-19.

Somani, U., Lakhani, K., Mundra, M.: Implementing digital signature with RSA encryption algorithm to enhance the data security of cloud in cloud computing. In: 1st International Conference on Parallel, Distributed and Grid Computing, pp. 211–216 (2010).

Sure, T. A. R. (2023). An analysis of telemedicine and virtual care trends on iOS platforms. Journal of Health Education Research & Development, 11(5)

Jang-Jaccard, J., Nepal, S.: A survey of emerging threats in cybersecurity. Journal of Computer and System Sciences 80(5), 973–993 (2014).

Liu, W., Ding, S., Zhang, X., Guo, Z., Liang, Y.: A survey of security and privacy issues in cloud computing based on big data. In: Recent Advances in Information Systems and Technologies, pp. 177–184. Springer (2017).

Conti, M., Dehghantanha, A., Franke, K., Watson, S.: Internet of Things security and forensics: Challenges and opportunities. Future Generation Computer Systems 78, 544–546 (2018).

Zaman, N., Zafar, M.R., Mehmood, H., Alharbi, H.: Anomaly detection in cloud server using machine learning techniques. Computers, Materials & Continua 64(1), 1–13 (2020).

Almomani, A., Anbar, M., Al-Akhras, M., Manasrah, A., Momani, O., Susanto, M., Avudaiappan, T.: AI-based resource provisioning for cloud computing environments: Towards efficient smart cities. Journal of Cloud Computing 10(1), 1–16 (2021).

Singh, A., Chana, I.: Q-aware: Quality of service based cloud resource provisioning. Computers & Electrical Engineering 47, 138–160 (2015).

Kumar, A. (2017). Machine learning approaches for adaptive assessment and prediction in network security situational awareness. Journal of Recent Trends in Computer Science and Engineering (JRTCSE), 5(2), 18-29.

Farahnakian, F., Liljeberg, P., Plosila, J.: Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 500–507 (2014).

Munir, A., Katib, I., Alsalemi, A., Mehmood, R., Jararweh, Y.: AI-based cyber defense for securing IoT and cloud technologies. Procedia Computer Science 151, 701–706 (2019).