Development of a Machine Learning-based Intrusion Detection System for Internet of Things (IoT) Networks

Thesis Abstract

Abstract
The proliferation of Internet of Things (IoT) devices has introduced new challenges in ensuring the security and privacy of data transmitted over networks. Intrusion detection systems play a crucial role in detecting and mitigating security threats in IoT networks. This thesis presents the development of a machine learning-based Intrusion Detection System (IDS) specifically designed for IoT networks. The system leverages the power of machine learning algorithms to detect and classify various types of intrusions in real-time, enhancing the overall security posture of IoT environments. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. The literature review in Chapter Two explores existing research and developments in the field of intrusion detection systems for IoT networks, highlighting the strengths and limitations of current approaches. Chapter Three details the research methodology employed in designing and implementing the machine learning-based IDS for IoT networks. The methodology includes data collection, preprocessing, feature selection, model training, evaluation, and validation processes. Various machine learning algorithms, such as decision trees, random forests, and support vector machines, are investigated for their effectiveness in detecting intrusions in IoT networks. Chapter Four presents a comprehensive discussion of the findings obtained from the experimental evaluation of the developed IDS. The performance metrics, including detection rate, false positive rate, and accuracy, are analyzed to assess the effectiveness of the system in detecting various types of intrusions. The chapter also discusses the practical implications of the findings and potential areas for future research and improvement. In Chapter Five, the thesis concludes with a summary of the key findings, contributions, and implications of the research. The limitations of the study are acknowledged, and recommendations for further research and system enhancements are provided. Overall, this thesis contributes to the field of cybersecurity by proposing a machine learning-based approach to enhancing the security of IoT networks through an effective and efficient intrusion detection system. Keywords Internet of Things, IoT networks, Intrusion Detection System, Machine Learning, Cybersecurity, Data Security, Network Security, Threat Detection, Security Systems.