Thesis Abstract

Abstract
The rapid growth of the Internet of Things (IoT) has led to an increase in the number of connected devices, making network security a critical concern. Intrusion detection plays a vital role in ensuring the security of IoT networks by identifying and responding to malicious activities. Traditional rule-based intrusion detection systems often struggle to keep up with the dynamic nature of IoT environments. In response to this challenge, machine learning techniques have emerged as a promising approach for enhancing intrusion detection in IoT networks. This thesis investigates the application of machine learning algorithms for intrusion detection in IoT networks. The research aims to develop a robust and efficient intrusion detection system that can effectively detect and respond to security threats in IoT environments. The study focuses on exploring the potential of machine learning models, such as support vector machines, neural networks, and decision trees, in improving the accuracy and efficiency of intrusion detection systems for IoT networks. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two conducts a comprehensive literature review, examining existing research on intrusion detection in IoT networks, machine learning algorithms, and their applications in cybersecurity. Chapter Three details the research methodology, including data collection, preprocessing, feature selection, model training, and evaluation metrics. The chapter also discusses the experimental setup and the datasets used for training and testing the machine learning models. Chapter Four presents a detailed discussion of the findings obtained from the experiments, including the performance evaluation of different machine learning algorithms for intrusion detection in IoT networks. The chapter analyzes the accuracy, detection rate, false positive rate, and other metrics to assess the effectiveness of the proposed approach. Finally, Chapter Five provides a summary of the research findings, conclusions drawn from the study, and recommendations for future research directions. The thesis contributes to the field of cybersecurity by demonstrating the potential of machine learning techniques in enhancing intrusion detection capabilities in IoT networks, thereby improving the overall security posture of IoT ecosystems. Keywords Internet of Things, IoT networks, intrusion detection, machine learning, cybersecurity, support vector machines, neural networks, decision trees.

Thesis Overview

The project titled "Applying Machine Learning for Intrusion Detection in IoT Networks" aims to address the increasing cybersecurity threats targeting Internet of Things (IoT) networks. As the number of connected devices continues to grow, so does the vulnerability of these networks to cyber attacks. Traditional security measures are often insufficient to protect IoT devices due to their resource constraints and diverse communication protocols. Therefore, this research focuses on leveraging machine learning techniques to enhance intrusion detection capabilities in IoT networks. The research will begin by providing an introduction to the growing importance of IoT networks and the security challenges they face. This will be followed by a detailed background study to explore existing intrusion detection methods and their limitations in the context of IoT environments. The problem statement will highlight the critical need for more advanced and efficient intrusion detection systems tailored for IoT networks. The objectives of the study include developing and implementing machine learning algorithms for real-time detection of intrusions in IoT networks. By utilizing supervised and unsupervised learning approaches, the research aims to enhance the accuracy and efficiency of intrusion detection while minimizing false positives. The study will also investigate the scalability of machine learning models in IoT environments with a large number of connected devices. Limitations of the study will be acknowledged, such as challenges related to data collection, model training, and deployment in resource-constrained IoT devices. The scope of the research will be defined to focus on specific types of cyber threats, network topologies, and machine learning algorithms suitable for intrusion detection in IoT networks. The significance of the study lies in its potential to improve the security posture of IoT ecosystems and protect sensitive data transmitted through these networks. By developing more robust intrusion detection mechanisms, organizations and individuals can better safeguard their IoT devices against cyber attacks and data breaches. The findings of this research are expected to contribute to the advancement of cybersecurity practices in the rapidly evolving IoT landscape. The structure of the thesis will be outlined to guide the reader through the research process, including chapters dedicated to literature review, research methodology, discussion of findings, and conclusion. Definitions of key terms related to intrusion detection, machine learning, and IoT networks will be provided to ensure clarity and understanding throughout the thesis.