Thesis Abstract

Abstract
The rapid growth of the Internet of Things (IoT) has led to an increased need for effective security measures to protect IoT networks from cyber threats. In this thesis, the focus is on applying machine learning techniques for intrusion detection in IoT networks. The primary objective is to develop a robust intrusion detection system that can accurately detect and mitigate various types of attacks targeting IoT devices and networks. The research begins with a comprehensive review of the existing literature in Chapter Two, which covers various aspects of IoT security, machine learning algorithms, and intrusion detection systems. This literature review serves as the foundation for understanding the current state-of-the-art in the field and identifying gaps that this research aims to address. Chapter Three details the research methodology employed in this study. It includes the selection of datasets, preprocessing techniques, feature selection methods, and the implementation of machine learning algorithms for intrusion detection. The chapter also discusses the evaluation metrics used to assess the performance of the proposed intrusion detection system. In Chapter Four, the findings of the research are presented and discussed in detail. This chapter includes the results of experiments conducted to evaluate the performance of the developed intrusion detection system. The effectiveness of different machine learning algorithms in detecting various types of attacks in IoT networks is analyzed, along with the overall performance metrics achieved. Finally, Chapter Five provides a conclusion and summary of the thesis. The key findings, contributions, and limitations of the research are discussed, along with recommendations for future work in this area. The significance of applying machine learning techniques for intrusion detection in IoT networks is highlighted, emphasizing the importance of enhancing security measures to safeguard IoT devices and networks from cyber threats. Overall, this thesis contributes to the advancement of research in IoT security by proposing an effective intrusion detection system based on machine learning techniques. The results obtained demonstrate the potential of machine learning in improving the security of IoT networks and highlight the importance of continuous research and development in this critical area of cybersecurity.

Thesis Overview

The research project titled "Applying Machine Learning Techniques for Intrusion Detection in Internet of Things (IoT) Networks" aims to explore and implement advanced machine learning algorithms to enhance the security of IoT networks. Internet of Things (IoT) devices have become increasingly prevalent in various domains, ranging from smart homes to industrial systems, offering numerous benefits through connectivity and automation. However, the widespread adoption of IoT devices has raised concerns about security vulnerabilities and potential cyber threats. The primary objective of this research is to develop a robust and efficient intrusion detection system for IoT networks using machine learning techniques. By leveraging the power of machine learning algorithms, such as deep learning, anomaly detection, and pattern recognition, the proposed system seeks to detect and mitigate various types of cyber attacks, including malware infections, denial-of-service attacks, and unauthorized access attempts. The research will begin with a comprehensive literature review to examine existing intrusion detection methods in IoT networks and evaluate the effectiveness of machine learning approaches in enhancing security measures. This review will provide a solid foundation for identifying gaps in the current research and formulating a novel approach to address the challenges of intrusion detection in IoT environments. Subsequently, the research methodology will involve collecting and analyzing real-world IoT network data to train and test the machine learning models. Various datasets will be used to simulate different attack scenarios and evaluate the performance of the intrusion detection system in detecting and responding to security threats effectively. The findings of the research will be presented and discussed in detail in Chapter Four, highlighting the performance metrics, accuracy rates, false positive rates, and other key indicators of the machine learning-based intrusion detection system. The discussion will also include a comparative analysis with existing intrusion detection methods to demonstrate the superiority and effectiveness of the proposed approach. In conclusion, the research project "Applying Machine Learning Techniques for Intrusion Detection in Internet of Things (IoT) Networks" aims to contribute to the field of cybersecurity by offering a cutting-edge solution to enhance the security posture of IoT ecosystems. By leveraging machine learning algorithms and advanced data analytics techniques, the proposed intrusion detection system has the potential to significantly improve the detection and prevention of cyber threats in IoT networks, thereby ensuring the integrity and confidentiality of sensitive data transmitted and processed by IoT devices.