Using Machine Learning to Detect and Prevent Cyber Attacks in IoT Networks

Thesis Abstract

Abstract
Cyber attacks on Internet of Things (IoT) networks have become a significant concern due to the widespread adoption of IoT devices in various domains. This thesis explores the application of machine learning techniques to detect and prevent cyber attacks in IoT networks. The research investigates the challenges associated with securing IoT networks, the limitations of traditional security mechanisms, and the potential of machine learning algorithms to enhance cybersecurity in 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 definition of key terms. The chapter sets the foundation for the study by highlighting the importance of addressing cybersecurity threats in IoT networks and the role of machine learning in improving detection and prevention mechanisms. Chapter Two presents a comprehensive literature review that examines existing research and approaches related to cybersecurity in IoT networks and the application of machine learning for threat detection. The review covers topics such as IoT network architecture, common cyber threats targeting IoT devices, traditional security measures, machine learning algorithms for anomaly detection, and previous studies on securing IoT networks using AI-based solutions. Chapter Three outlines the research methodology employed in this study, including data collection methods, dataset selection, machine learning algorithm selection, model training and evaluation techniques, and the experimental setup for testing the proposed approach. The chapter also discusses the ethical considerations and challenges associated with conducting research in the field of cybersecurity and machine learning. Chapter Four presents a detailed discussion of the research findings, including the performance evaluation of the machine learning model in detecting and preventing cyber attacks in IoT networks. The chapter analyzes the results obtained from the experiments, discusses the strengths and limitations of the proposed approach, and provides insights into the effectiveness of using machine learning for enhancing cybersecurity in IoT environments. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and highlighting future research directions in the field of IoT security and machine learning. The chapter also offers recommendations for improving the detection and prevention of cyber attacks in IoT networks and emphasizes the importance of continuous research and innovation in enhancing cybersecurity measures for IoT devices. In conclusion, this thesis contributes to the growing body of knowledge on leveraging machine learning techniques to enhance cybersecurity in IoT networks. By addressing the challenges of detecting and preventing cyber attacks in IoT environments, this research aims to improve the overall security posture of IoT devices and networks, ultimately enhancing the trust and reliability of IoT-based systems in various applications.