Thesis Abstract

Abstract
The rapid growth of Internet of Things (IoT) devices has led to an increased risk of security threats and vulnerabilities within networks. Intrusion detection plays a crucial role in identifying and mitigating these threats to ensure the security and integrity of IoT systems. This thesis focuses on the application of machine learning algorithms for intrusion detection in IoT networks. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Definitions of key terms are also provided to establish a common understanding. Chapter Two conducts a comprehensive literature review, covering ten key aspects related to intrusion detection, machine learning algorithms, IoT networks, and their intersection. This chapter critically analyzes existing research, identifies gaps, and lays the foundation for the research methodology. Chapter Three outlines the research methodology employed in this study. It includes detailed descriptions of data collection methods, dataset preparation, feature selection, algorithm selection, model training and evaluation, performance metrics, and validation techniques. The chapter also discusses ethical considerations and potential biases in the research process. Chapter Four presents an in-depth discussion of the findings obtained through the application of machine learning algorithms for intrusion detection in IoT networks. The chapter highlights the performance of different algorithms, their strengths and limitations, and provides insights into the effectiveness of the proposed approach. Chapter Five concludes the thesis by summarizing the key findings, discussing implications for practice and future research directions. The study emphasizes the importance of leveraging machine learning techniques for enhancing security in IoT networks and proposes recommendations for improving intrusion detection mechanisms. Overall, this thesis contributes to the field of cybersecurity by showcasing the efficacy of machine learning algorithms in detecting and preventing intrusions in IoT networks. The research findings provide valuable insights for practitioners, researchers, and policymakers seeking to enhance the security of IoT systems and protect against evolving cyber threats.

Thesis Overview

The project titled "Applying Machine Learning Algorithms for Intrusion Detection in IoT Networks" focuses on enhancing the security of Internet of Things (IoT) networks through the utilization of machine learning algorithms. IoT networks are increasingly becoming pervasive in various sectors, connecting numerous devices to facilitate data exchange and automation. However, the interconnected nature of IoT devices also makes them vulnerable to cyber threats and malicious activities. Intrusion detection plays a crucial role in identifying and mitigating such security threats in IoT networks. Machine learning algorithms offer a promising approach to enhance intrusion detection capabilities in IoT networks by enabling systems to learn from data patterns and detect anomalies or malicious activities in real-time. This project aims to explore the effectiveness of various machine learning algorithms in detecting intrusions in IoT networks and to develop a robust intrusion detection system tailored to the unique characteristics of IoT environments. The research will begin with a comprehensive literature review to examine existing studies, methodologies, and technologies related to intrusion detection in IoT networks and machine learning algorithms. Subsequently, the research methodology will be outlined, including data collection, preprocessing, feature selection, algorithm selection, and model evaluation processes. The project will involve the implementation and testing of different machine learning algorithms such as support vector machines, random forests, neural networks, and clustering algorithms on IoT network datasets to evaluate their performance in detecting intrusions. The findings from these experiments will be thoroughly analyzed and discussed in Chapter Four to identify the strengths and limitations of each algorithm and their suitability for intrusion detection in IoT networks. In conclusion, the project will provide insights into the efficacy of machine learning algorithms for enhancing intrusion detection in IoT networks and propose recommendations for developing more robust and adaptive security mechanisms in IoT environments. By addressing the security challenges associated with IoT networks, this research aims to contribute to the advancement of cybersecurity practices in the rapidly evolving landscape of interconnected devices and services.