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Introduction

In recent years, the rise of cyber threats and attacks has posed significant challenges to organizations across the globe. Traditional security measures are no longer sufficient to protect sensitive data and networks from sophisticated cyber attacks. As a result, there is a growing need for advanced technologies such as Artificial Intelligence (AI) to enhance network security Information and Event Management (SIEM) systems.

This thesis explores the use of AI-driven network security Information and Event Management (SIEM) to improve the detection and response to cyber threats. By leveraging AI algorithms and machine learning techniques, organizations can analyze large volumes of security data in real-time to identify patterns and anomalies that may indicate a potential security breach. This proactive approach can help organizations to detect and mitigate security threats before they escalate into a full-blown attack.

The primary objective of this research is to investigate the effectiveness of AI-driven SIEM solutions in enhancing network security and reducing the impact of cyber threats. By analyzing existing literature, conducting case studies, and exploring real-world implementations, this research aims to provide valuable insights into the benefits and limitations of AI-driven SIEM for network security.

The remainder of this thesis is structured as follows:

Chapter 1: Introduction
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 Overview of Network Security and SIEM
2.2 Evolution of AI in Network Security
2.3 Current Trends in AI-driven SIEM
2.4 Benefits of AI-driven SIEM
2.5 Challenges of AI-driven SIEM
2.6 Case Studies on AI-driven SIEM Implementation
2.7 Comparison of AI-driven SIEM Solutions
2.8 Regulatory Compliance and AI-driven SIEM
2.9 Ethical Considerations in AI-driven SIEM
2.10 Future Research Directions

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Case Study Selection
3.5 Ethical Considerations
3.6 Limitations of the Research
3.7 Validity and Reliability
3.8 Data Privacy and Confidentiality

Chapter 4: Discussion of Findings
4.1 Analysis of Case Studies
4.2 Effectiveness of AI-driven SIEM
4.3 Challenges and Limitations
4.4 Recommendations for Implementation
4.5 Comparison with Traditional SIEM
4.6 Future Prospects of AI-driven SIEM

Chapter 5: Conclusion
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Implications for Practice
5.4 Recommendations for Future Research

This thesis aims to contribute to the growing body of research on AI-driven network security Information and Event Management (SIEM) by providing a comprehensive analysis of its benefits, challenges, and potential applications. By exploring the intersection of AI and network security, this research seeks to advance the field of cybersecurity and help organizations better protect their data and networks from cyber threats.

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