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Empowering Cloud Security Through Advanced Monitoring and Analytics Techniques
Abstract
The rapid adoption of cloud computing has transformed data management, introducing security challenges requiring advanced monitoring and analytics. This study explores techniques for threat detection, investigation, and response in cloud security. It covers log aggregation, event correlation, and anomaly detection for comprehensive visibility. Machine learning and AI enable sophisticated threat detection and automation, while threat intelligence and user behavior analytics identify insider threats. Security orchestration platforms streamline operations, with Infrastructure as Code embedding security into provisioning. The research examines SOAR platforms and real-world implementations through case studies. Emerging challenges include serverless and edge computing. This analysis guides organizations in protecting cloud assets, combining theory and practice for robust security in our complex digital landscape.
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“These themes are especially relevant in addressing contemporary concerns such as model explainability, robustness to manipulation, and privacy-preserving detection in distributed environments [25,44,49].”
From Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review (2025) · cites this paper as [44]
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Research works printed but not linked: 29
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Cited by
Sorted by citations
Ofori, H. K., Bell-Dzide, K., Brown-Acquaye, W. L., Lempogo, F., Frimpong, S. O., Agbehadji, I. E., & Millham, R. C. (2025) · Symmetry · doi:10.3390/sym17101704 · Cited by 7
Metadata
- Authors
- Kumar, T., Minakshi, & Joshi, K.
- Affiliations
- Galgotias University (IN)
- King Khalid University, Saudi Arabia
- Uttaranchal University (IN)
- Venue
- Risk-Based Approach to Secure Cloud Migration
- Publisher
- IGI Global Scientific Publishing
- Published
- 2025-04-16
- Pages
- 163-186
- OpenAlex
- W4409494065
- Cited by
- 2
- References
- 0
- Citations by year
- 2025: 2
- FWCI
- 5.51
- Type
- book chapter
- Language
- ng
- Open access
- closed
- Locations
- IGI Global eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- cloud security, security monitoring, security analytics, threat detection, log aggregation, event correlation, anomaly detection, machine learning for security, artificial intelligence for security, threat intelligence, user behavior analysis, insider threats, security orchestration, infrastructure as code, serverless computing, edge computing
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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