Applying Machine Learning Algorithms for Intrusion Detection in IoT Networks

Thesis Abstract

Abstract
The widespread integration of Internet of Things (IoT) devices in various applications has brought about numerous benefits, but it has also introduced new security challenges. One critical aspect of IoT security is the detection of intrusions to prevent unauthorized access and protect sensitive data. In this thesis, we focus on applying machine learning algorithms for intrusion detection in IoT networks. The primary objective is to develop a robust and efficient intrusion detection system that can effectively identify and respond to security threats in IoT environments. The thesis begins with a comprehensive introduction that outlines the background of the study, the problem statement, research objectives, limitations, scope, significance of the study, and the structure of the thesis. The introduction also provides definitions of key terms related to intrusion detection and machine learning in IoT networks. Chapter two presents a detailed literature review that explores existing research on intrusion detection systems, machine learning algorithms, and their application in IoT security. The review covers various approaches, methodologies, and tools used in intrusion detection and highlights the strengths and limitations of different techniques. Chapter three outlines the research methodology employed in this study, including data collection, preprocessing, feature selection, model training, and evaluation techniques. The chapter also discusses the dataset used for experimentation and the metrics used to evaluate the performance of the intrusion detection system. In chapter four, the findings of the study are presented and discussed in detail. The chapter includes the results of experiments conducted to evaluate the performance of the machine learning algorithms in detecting intrusions in IoT networks. A comparative analysis of different algorithms is also provided to identify the most effective approach for intrusion detection. The final chapter, chapter five, presents the conclusion and summary of the thesis. The chapter discusses the key findings, contributions, limitations, and future research directions. The conclusion emphasizes the importance of leveraging machine learning techniques for enhancing security in IoT networks and provides recommendations for further improving intrusion detection systems. Overall, this thesis contributes to the field of IoT security by demonstrating the effectiveness of machine learning algorithms in detecting intrusions and enhancing the overall security posture of IoT environments. The findings of this study have practical implications for the development of more robust and efficient intrusion detection systems to safeguard IoT networks from security threats.