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Introduction

The rapid advancement of technology has led to an exponential increase in network traffic, which has made it challenging for network administrators to effectively monitor and classify network traffic. Network traffic classification plays a crucial role in network security, quality of service, and traffic engineering. Machine learning-based approaches have shown great potential in addressing these challenges by automatically classifying network traffic based on its characteristics. In this thesis, we focus on developing a machine learning-based approach for network traffic classification using semi-supervised learning.

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Network Traffic Classification
2.2 Machine Learning in Network Traffic Classification
2.3 Semi-Supervised Learning
2.4 Previous Studies on Semi-Supervised Learning in Network Traffic Classification
2.5 Challenges in Network Traffic Classification
2.6 Evaluation Metrics for Network Traffic Classification
2.7 Applications of Network Traffic Classification
2.8 Trends in Network Traffic Classification
2.9 Future Directions in Network Traffic Classification
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Semi-Supervised Learning Algorithm Selection
3.6 Model Training
3.7 Model Evaluation
3.8 Performance Metrics
3.9 Validation Techniques
3.10 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Evaluation of Semi-Supervised Learning Model
4.2 Comparison with Supervised Learning Models
4.3 Impact of Feature Selection on Classification Performance
4.4 Interpretation of Model Results
4.5 Practical Implications of Findings
4.6 Limitations of the Study
4.7 Future Research Directions
4.8 Recommendations for Network Administrators
4.9 Conclusions
4.10 Summary of Findings

Chapter 5: Conclusion and Summary
5.1 Summary of Contributions
5.2 Conclusion
5.3 Implications for Practice
5.4 Implications for Research
5.5 Limitations of the Study
5.6 Recommendations for Future Research
5.7 Conclusion

Thesis Overview

Developing a machine learning-based approach for network traffic classification using semi-supervised learning is becoming increasingly essential in the field of network security and management. This thesis aims to address the challenges faced by network administrators in classifying network traffic accurately and efficiently. By leveraging the power of semi-supervised learning algorithms, we seek to develop a robust and scalable solution for network traffic classification.

The literature review will provide a comprehensive overview of network traffic classification, machine learning techniques, semi-supervised learning, and previous studies in the field. It will also highlight the challenges, evaluation metrics, applications, trends, and future directions in network traffic classification.

The research methodology chapter will detail the design, data collection, preprocessing, feature selection, algorithm selection, model training, evaluation, performance metrics, validation techniques, and ethical considerations involved in developing our machine learning-based approach.

The discussion of findings chapter will analyze the results of our experiments, compare different models, evaluate the impact of feature selection, interpret model results, discuss practical implications, outline limitations, suggest future research directions, and provide recommendations for network administrators.

The conclusion and summary chapter will summarize the contributions, conclude the study, discuss implications for practice and research, highlight limitations, recommend future research, and provide a final conclusion.

Overall, this thesis will contribute to the existing body of knowledge in the field of network traffic classification and provide valuable insights for network administrators, researchers, and industry practitioners.

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