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Introduction

With the increasing sophistication and frequency of cyber attacks, intrusion detection systems (IDS) have become a crucial component in safeguarding network security. Traditional IDS solutions often face challenges such as high false alarm rates, inability to detect novel attacks, and scalability issues. Federated learning has emerged as a promising approach to address these challenges by allowing multiple entities to collaboratively train a global model while keeping their data local and private. This thesis focuses on the application of secure federated learning for collaborative intrusion detection, aiming to enhance the detection accuracy and efficiency of IDS systems.

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 Two: Literature Review
2.1 Intrusion Detection Systems
2.2 Machine Learning in Intrusion Detection
2.3 Federated Learning
2.4 Secure Federated Learning
2.5 Collaborative Intrusion Detection
2.6 Existing Approaches
2.7 Challenges and Limitations
2.8 Opportunities for Improvement
2.9 Summary of Literature Reviewed
2.10 Research Gap

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Model Architecture Design
3.5 Encryption Techniques
3.6 Federated Learning Algorithm
3.7 Evaluation Metrics
3.8 Experiment Setup
3.9 Ethical Considerations
3.10 Data Analysis Techniques

Chapter Four: Discussion of Findings
4.1 Performance Evaluation
4.2 Comparison with Baseline Models
4.3 Impact of Collaboration on Detection Accuracy
4.4 Robustness to Adversarial Attacks
4.5 Scalability and Efficiency
4.6 Privacy Preservation
4.7 Interpretability of Model
4.8 Future Research Directions

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

Thesis Overview: Secure federated learning for collaborative intrusion detection

Cybersecurity threats continue to evolve, posing serious challenges to the security of network infrastructures. Intrusion Detection Systems (IDS) play a crucial role in detecting and preventing unauthorized access or malicious activities in computer networks. However, traditional IDS solutions face limitations such as high false alarm rates, inability to detect novel attacks, and scalability issues. In response to these challenges, federated learning has emerged as a promising approach to enhance the accuracy and efficiency of IDS systems.

This thesis focuses on the application of secure federated learning for collaborative intrusion detection, aiming to address the shortcomings of existing IDS solutions and improve the overall security posture of network environments. The study will begin with an overview of the background and context of intrusion detection systems, machine learning in intrusion detection, federated learning, and secure federated learning. A comprehensive literature review will be conducted to examine existing approaches, challenges, opportunities for improvement, and research gaps in the field.

The research methodology will outline the approach taken to design and implement a secure federated learning framework for collaborative intrusion detection. This will include data collection, preprocessing, model architecture design, encryption techniques, federated learning algorithm selection, evaluation metrics, experiment setup, ethical considerations, and data analysis techniques. The findings of the study will be discussed in detail, focusing on performance evaluation, comparison with baseline models, impact of collaboration on detection accuracy, robustness to adversarial attacks, scalability, efficiency, privacy preservation, and interpretability of the model.

In conclusion, this thesis will provide a summary of the findings, contributions of the study, implications for practice, limitations, recommendations for future research, and a comprehensive conclusion. Through this research, we aim to contribute to the advancement of secure federated learning for collaborative intrusion detection and provide valuable insights for enhancing network security in the face of evolving cyber threats.

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