Evaluating One-Class LSTM Models for Real-Time Cybersecurity Threat Detection

1. Introduction 1.1 Background and Motivation 1.2 Research Objectives 1.3 Methodological Approach 1.4 Structure of the Thesis 2. Literature Review 2.1 Cybersecurity Threat Detection 2.2 Machine Learning in Cybersecurity 2.3 LSTM Models in Anomaly Detection 2.4 One-Class Classification Techniques 2.5 Real-Time Threat Detection Challenges 3. Theoretical Framework 3.1 Long Short-Term Memory (LSTM) Networks 3.2 One-Class Classification Theory 3.3 Evaluation Metrics for Model Performance 3.4 Data Preprocessing Methods 4. Methodology 4.1 Research Design Overview 4.2 Dataset Description and Preparation 4.3 Model Development Process 4.4 Implementation Tools and Environments 4.5 Evaluation Strategy 5. Experimental Setup 5.1 Model Training Details 5.2 Testing Protocols 5.3 Parameter Tuning and Optimization 5.4 Real-Time Processing Implementation 6. Results and Analysis 6.1 Model Performance Metrics 6.2 Comparative Analysis 6.3 Error Analysis and Interpretation 6.4 Scalability and Efficiency Assessment 7. Discussion 7.1 Implications of Findings 7.2 Limitations of the Study 7.3 Potential for Future Research 7.4 Ethical Considerations 8. Conclusion 8.1 Summary of Contributions 8.2 Practical Recommendations 8.3 Final Thoughts and Outlook