Analyzing the Efficacy of Machine Learning in Detecting Insider Threats in Cloud Environments

1. Introduction 1.1 Background and Motivation 1.2 Research Objectives 1.3 Scope and Limitations 1.4 Structure of the Thesis 2. Literature Review 2.1 Overview of Insider Threats 2.2 Machine Learning in Security 2.3 Cloud Environment Vulnerabilities 3. Methodology 3.1 Research Design 3.2 Data Collection Techniques 3.3 Model Selection Criteria 4. Machine Learning Techniques 4.1 Supervised Learning Models 4.2 Unsupervised Learning Models 4.3 Hybrid Approaches 5. Case Studies and Experiments 5.1 Case Study Selection 5.2 Experimental Setup 5.3 Results and Observations 6. Evaluation of Results 6.1 Metrics for Evaluation 6.2 Comparative Analysis 6.3 Discussion of Findings 7. Challenges and Limitations 7.1 Data Quality and Availability 7.2 Model Interpretability Issues 7.3 Scalability Challenges 8. Conclusion and Future Work 8.1 Summary of Key Findings 8.2 Recommendations for Practice 8.3 Directions for Future Research