Evaluating Machine Learning Techniques for Cybersecurity Applications in Identifying Network Vulnerabilities

1. Introduction 1.1 Background and Motivation 1.2 Importance of Cybersecurity 1.3 Objectives of the Study 1.4 Structure of the Paper 2. Overview of Cybersecurity 2.1 Definition and Scope 2.2 Common Security Threats 2.3 Network Vulnerabilities 3. Machine Learning in Cybersecurity 3.1 Role of Machine Learning 3.2 Popular Techniques in Use 3.3 Benefits and Challenges 4. Evaluation Criteria 4.1 Performance Metrics 4.2 Data Requirements 4.3 Evaluation Methodologies 5. Machine Learning Techniques 5.1 Supervised Learning Approaches 5.2 Unsupervised Learning Methods 5.3 Deep Learning Applications 5.4 Reinforcement Learning 6. Case Studies 6.1 Intrusion Detection Systems 6.2 Anomaly Detection Techniques 6.3 Botnet Identification 7. Challenges and Limitations 7.1 Data Quality and Availability 7.2 Computational Cost 7.3 Limitations of Current Models 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Recommendations for Improvement 8.3 Directions for Future Research