Thesis Abstract

Abstract
As the Internet of Things (IoT) continues to expand rapidly, ensuring the security and integrity of IoT networks has become increasingly critical. Intrusion detection plays a vital role in identifying and mitigating potential threats to IoT systems. This thesis explores the application of machine learning techniques for enhancing intrusion detection in IoT networks. The research aims to develop a robust intrusion detection system that can effectively detect and respond to malicious activities in IoT environments. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to intrusion detection, machine learning, and IoT security. The review synthesizes existing research and identifies gaps that this study aims to address. Chapter 3 details the research methodology employed in this study, including data collection techniques, feature selection, model development, and evaluation metrics. The chapter also discusses the dataset used for training and testing the intrusion detection models and explains the rationale behind the chosen methodologies. In Chapter 4, the findings of the research are extensively discussed, highlighting the performance of various machine learning algorithms in detecting intrusions in IoT networks. The chapter presents a comparative analysis of different models, evaluating their accuracy, precision, recall, and other relevant metrics. The results provide insights into the strengths and limitations of each approach, aiding in the selection of the most effective intrusion detection system. Finally, Chapter 5 presents the conclusion and summary of the thesis, summarizing the key findings, contributions, and implications of the research. The chapter also discusses future research directions and recommendations for further enhancing intrusion detection in IoT networks using machine learning techniques. Overall, this thesis contributes to the field of IoT security by demonstrating the efficacy of machine learning in improving intrusion detection capabilities. The research outcomes offer valuable insights for practitioners, researchers, and policymakers working to safeguard IoT ecosystems from cyber threats, ultimately enhancing the security and resilience of interconnected devices and systems.

Thesis Overview