Advanced International Journal of Multidisciplinary Research

E-ISSN: 2584-0487

An Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 2 Issue 5 September-October 2024 Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 2 Issue 5
September-October 2024

Submit your research paper

Enhancing Cloud Compliance: A Machine Learning Approach

Author(s) Jesu Narkarunai Arasu Malaiyappan, Sanjeev Prakash, Samir Vinayak Bayani, Munivel Devan
Country United States
Abstract The intersection of machine learning (ML) and cloud computing presents significant opportunities to enhance cloud compliance and security practices. This research paper explores the role of ML in improving cloud compliance, focusing on proactive threat detection, automated incident response, and adaptive security controls. The importance of ML-driven approaches lies in their ability to analyse large datasets, detect anomalies, and mitigate risks in dynamic cloud environments. Methods employed include case studies and experiments showcasing real-world applications of ML in cloud security, such as Google Cloud's Context-Aware Access and AWS GuardDuty for threat detection. Experimental findings demonstrate the effectiveness of ML models in reducing mean time to detect (MTTD) security incidents and improving incident response capabilities.
Results highlight the transformative impact of ML technologies in bolstering cloud security effectiveness and resilience. ML-powered compliance monitoring systems, like Netflix's, have significantly improved compliance posture while reducing operational costs. Implications of this research include enhanced security governance, reduced compliance risks, and improved operational efficiencies within cloud infrastructures. Future directions entail exploring advanced ML techniques, addressing ethical considerations, and integrating ML-driven security frameworks into holistic cloud governance strategies.
Keywords machine learning, cloud compliance, cloud security, proactive threat detection, automated incident response, adaptive security controls, real-world case studies, future directions
Discipline Computer > AI / ML
Published In Volume 2, Issue 2, March-April 2024
Published On 2024-04-24
Cite This Enhancing Cloud Compliance: A Machine Learning Approach - Jesu Narkarunai Arasu Malaiyappan, Sanjeev Prakash, Samir Vinayak Bayani, Munivel Devan - AIJMR Volume 2, Issue 2, March-April 2024. DOI 10.62127/aijmr.2024.v02i02.1036
DOI https://doi.org/10.62127/aijmr.2024.v02i02.1036
Short DOI https://doi.org/gtrsr5
View / Download Full-text Research PDF File

Share this



E-ISSN 2584-0487
Downloads
Cover Page Vol 2 Isu 5 Cover Page Vol 2 Isu 4 Cover Page Vol 2 Isu 3 Research Paper Format

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9328-605090 editor@aijmr.com