Cybersecurity in Cloud Computing: Leveraging AI for Enhanced Data Protection and Privacy

Main Article Content

Anrayan Kunar

Abstract

As cloud computing becomes ubiquitous, ensuring data security and privacy is paramount. This paper investigates the role of AI in enhancing cybersecurity measures for cloud environments, focusing on data protection, access control, and compliance. Our study proposes a machine learning model that analyzes user behaviors and access patterns to detect unauthorized access and data breaches. The findings highlight the model's effectiveness in reducing incidents of data loss and improving compliance with data protection regulations, showcasing how AI can strengthen cybersecurity in cloud computing architectures.

Downloads

Download data is not yet available.

Article Details

How to Cite
Cybersecurity in Cloud Computing: Leveraging AI for Enhanced Data Protection and Privacy. (2024). International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/85
Section
Articles

How to Cite

Cybersecurity in Cloud Computing: Leveraging AI for Enhanced Data Protection and Privacy. (2024). International Meridian Journal, 6(6). https://meridianjournal.in/index.php/IMJ/article/view/85

References

Balantrapu, S. S. (2024). Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection. International Journal of Sustainable Development Through AI, ML and IoT, 3(2), 1-15.

Balantrapu, S. S. (2023). Evaluating the Effectiveness of Machine Learning in Phishing Detection. International Scientific Journal for Research, 5(5).

Balantrapu, S. S. (2023). Future Trends in AI and Machine Learning for Cybersecurity. International Journal of Creative Research In Computer Technology and Design, 5(5).

Balantrapu, S. S. (2024). A Comprehensive Review of AI Applications in Cybersecurity. International Machine learning journal and Computer Engineering, 7(7).

Balantrapu, S. (2023). Cybersecurity Frameworks Enhanced by Machine Learning Techniques. International Journal of Sustainable Development in Computing Science, 5(4), 1-19. Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/584

Balantrapu, S. S. (2021). A Systematic Review Comparative Analysis of Machine Learning Algorithms for Malware Classification. International Scientific Journal for Research, 3(3), 1-29.

Balantrapu, S. S. (2020). AI-Driven Cybersecurity Solutions: Case Studies and Applications. International Journal of Creative Research In Computer Technology and Design, 2(2).

Balantrapu, S. S. (2024). AI for Predictive Cyber Threat Intelligence. International Journal of Management Education for Sustainable Development, 7(7), 1-28.

Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.

Balantrapu, S. S. (2022). Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study. International Journal of Sustainable Development Through AI, ML and IoT, 1(1), 1-15.

Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).

Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.

Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.

Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., Perumal, A. P., & Gopal, S. K. (2023). Beyond the Bin: Machine Learning-Driven Waste Management for a Sustainable Future. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 11(1), 16-27.

Deekshith, A. (2022). Cross-Disciplinary Approaches: The Role of Data Science in Developing AI-Driven Solutions for Business Intelligence. International Machine learning journal and Computer Engineering, 5(5).

Deekshith, A. (2021). Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models. International Journal of Management Education for Sustainable Development, 4(4), 1-33.

Deekshith, A. (2023). Scalable Machine Learning: Techniques for Managing Data Volume and Velocity in AI Applications. International Scientific Journal for Research, 5(5).

Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).

Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.

Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).

Boppiniti, S. T. (2023). Data Ethics in AI: Addressing Challenges in Machine Learning and Data Governance for Responsible Data Science. International Scientific Journal for Research, 5(5).

Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).

Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).

Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).

Most read articles by the same author(s)

1 2 3 4 > >>