Applying Machine Learning for Intrusion Detection in Internet of Things (IoT) Networks

Thesis Abstract

Abstract
The rapid proliferation of Internet of Things (IoT) devices in various domains has raised concerns about the security of these interconnected networks. Intrusion detection is a critical component of ensuring the security and integrity of IoT systems. Traditional rule-based intrusion detection systems may not be effective in detecting sophisticated and evolving cyber threats in IoT environments. Machine Learning (ML) techniques have emerged as a promising approach for enhancing intrusion detection capabilities in IoT networks. This thesis explores the application of ML algorithms for intrusion detection in IoT networks, aiming to improve the detection accuracy and efficiency while reducing false positives. Chapter 1 provides an introduction to the research topic, presenting the background of the study that highlights the increasing vulnerabilities in IoT networks. The problem statement identifies the limitations of traditional intrusion detection systems in IoT environments and sets the objectives of the study to leverage ML for enhancing security. The scope of the study delineates the boundaries within which the research is conducted, while the significance of the study emphasizes the potential impact of ML-based intrusion detection on IoT security. The chapter concludes with an overview of the thesis structure and definitions of key terms. Chapter 2 presents a comprehensive literature review on intrusion detection systems, IoT security challenges, and ML techniques relevant to the research topic. The review encompasses ten key areas, including the evolution of IoT networks, common cyber threats, existing intrusion detection approaches, and recent advancements in ML algorithms for security applications. Chapter 3 outlines the research methodology employed in this study, detailing the data collection process, dataset preparation, feature selection, ML model selection, training, and evaluation strategies. The chapter also discusses the implementation of the intrusion detection system in a simulated IoT environment and the metrics used to assess the performance of the ML models. Chapter 4 delves into the discussion of findings obtained from the experimental evaluation of ML-based intrusion detection in IoT networks. The chapter analyzes the detection accuracy, false positive rates, and computational efficiency of different ML algorithms, highlighting their strengths and limitations in detecting various types of cyber threats in IoT environments. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and suggesting future directions for enhancing ML-based intrusion detection in IoT networks. The chapter also underscores the significance of the study in advancing the security of IoT systems and mitigating cyber risks associated with interconnected devices. In conclusion, this thesis contributes to the growing body of research on enhancing IoT security through the application of ML techniques for intrusion detection. By leveraging the capabilities of ML algorithms, this study aims to bolster the resilience of IoT networks against evolving cyber threats, ultimately promoting a more secure and reliable IoT ecosystem.