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Introduction

In recent years, the Internet of Things (IoT) has gained significant popularity due to its ability to connect a wide range of devices and enable them to communicate and share data. However, the increasing connectivity of IoT devices also brings about security vulnerabilities that can be exploited by malicious actors. As a result, there is a critical need for effective threat detection and response mechanisms to protect IoT networks from cyber attacks.

Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the security of IoT networks by enabling automated threat detection and response. AI-driven solutions can analyze large volumes of data in real-time, identify suspicious patterns and behaviors, and respond to security incidents rapidly. This thesis aims to explore the application of AI in threat detection and response for IoT networks and evaluate its effectiveness in mitigating cybersecurity risks.

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 Evolution of IoT Security
2.2 Threat Landscape in IoT Networks
2.3 AI Techniques for Threat Detection
2.4 AI-based Intrusion Detection Systems
2.5 AI-driven Response Mechanisms
2.6 Challenges in AI-driven Threat Detection
2.7 Case Studies on AI in IoT Security
2.8 Ethical Considerations in AI-driven Security
2.9 Future Trends in AI and IoT Security
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 AI Models and Algorithms
3.5 Simulation Environments
3.6 Evaluation Metrics
3.7 Ethical Considerations
3.8 Limitations of Research Methodology

Chapter 4: Discussion of Findings
4.1 Analysis of AI-driven Threat Detection
4.2 Evaluation of Response Mechanisms
4.3 Effectiveness of AI Models
4.4 Comparison with Traditional Security Approaches
4.5 Impact on IoT Network Performance
4.6 Practical Implementation Challenges
4.7 Recommendations for Future Research
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Implications for Practice
5.4 Limitations of the Study
5.5 Recommendations for Future Work
5.6 Conclusion

Thesis Overview
The rapid growth of IoT networks has raised concerns about security risks and vulnerabilities that threaten the confidentiality, integrity, and availability of data. Traditional security mechanisms are no longer sufficient to defend against sophisticated cyber attacks targeting IoT devices. This thesis focuses on the application of AI-driven solutions for improving threat detection and response in IoT networks. By leveraging AI technologies such as machine learning, deep learning, and natural language processing, organizations can enhance their cybersecurity posture and mitigate the risks associated with IoT deployments. Through a comprehensive review of the literature, an analysis of research methodologies, and a discussion of empirical findings, this thesis aims to provide valuable insights into the effectiveness of AI in protecting IoT networks from cyber threats. The ultimate goal is to contribute to the development of more robust and resilient security measures that can safeguard the growing ecosystem of connected devices in the IoT landscape.

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