Evaluating Machine Learning Techniques in Detecting Insider Threats in Corporate IT Environments

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Research Objectives 1.4 Structure of the Paper 2. Literature Review 2.1 Overview of Insider Threats 2.2 Machine Learning in Cybersecurity 2.3 Previous Studies on Insider Threats 2.4 Challenges in Detection 3. Machine Learning Techniques Overview 3.1 Supervised Learning Models 3.2 Unsupervised Learning Models 3.3 Anomaly Detection Techniques 3.4 Hybrid Approaches 4. Data Collection and Preprocessing 4.1 Data Sources and Description 4.2 Data Cleaning Methods 4.3 Feature Selection and Extraction 4.4 Handling Class Imbalance 4.5 Dataset Splitting Strategy 5. Evaluation Metrics for Detection 5.1 Accuracy and Precision 5.2 Recall and F-Score 5.3 ROC Curve Analysis 5.4 Confusion Matrix Analysis 6. Experimental Setup and Deployment 6.1 Environment and Tools 6.2 Model Training Process 6.3 Evaluation Procedures 6.4 Deployment Scenarios 7. Results and Discussion 7.1 Comparative Analysis of Techniques 7.2 Key Findings and Observations 7.3 Limitations and Challenges 7.4 Implications for Practice 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Contributions to the Field 8.3 Recommendations 8.4 Directions for Future Research