ML-Driven Cloud Security Risk Assessment

Objectives to cover:

• Introduction: Overview of cloud computing growth and rising need for security.

• Background of Cloud Security: Fundamentals and importance of protecting cloud environments.

• Cloud Security Challenges and Risks: Key vulnerabilities, threats, and compliance concerns.

• Role of Machine Learning in Cybersecurity: How ML enhances detection and prediction of risks.

• Machine Learning Algorithms for Risk Assessment: Common techniques like SVM, Random Forest, and Neural Networks.

• Proposed Framework for Cloud Security Risk Assessment: Structured approach integrating ML models.

• Data Collection and Preprocessing: Gathering and preparing relevant cloud security datasets.

• Implementation and Evaluation: Applying ML models with metrics like accuracy, precision, and recall.

• Conclusion: Summary of findings, limitations, and future research directions.

Reference:  IEEE Style