Thesis Abstract

Abstract
The rapid proliferation of Internet of Things (IoT) devices has introduced numerous security challenges, highlighting the need for robust intrusion detection systems to safeguard these networks. This thesis presents the development of a Machine Learning-Based Intrusion Detection System (ML-IDS) specifically tailored for IoT networks. The primary objective of this research is to enhance the security posture of IoT environments by leveraging the power of machine learning algorithms to detect and mitigate potential intrusions effectively. Chapter One provides an introduction to the research topic, outlining the background of the study and presenting the problem statement. The objectives of the study are clearly defined, along with the limitations and scope of the research. The significance of the study is emphasized, and the structure of the thesis is outlined to guide the reader through the subsequent chapters. In Chapter Two, a comprehensive literature review is conducted to explore existing research and implementations related to intrusion detection systems, machine learning in cybersecurity, and IoT network security. The review covers ten key areas, providing a solid foundation for the development of the ML-IDS. Chapter Three delves into the research methodology employed in this study. The chapter discusses the dataset used for training and testing the ML-IDS, the selection and evaluation of machine learning algorithms, feature selection techniques, model training and validation processes, and the overall experimental setup. Chapter Four presents a detailed discussion of the findings obtained through the development and testing of the ML-IDS. The chapter analyzes the performance metrics, such as detection rate, false positive rate, and accuracy, to evaluate the efficacy of the system in detecting intrusions within IoT networks. The results are compared with existing approaches to highlight the strengths and limitations of the proposed system. Finally, Chapter Five concludes the thesis by summarizing the key findings and contributions of the research. The implications of the study are discussed, and recommendations for future work in this field are provided. The thesis concludes with a reflection on the significance of the ML-IDS in enhancing the security of IoT networks and the potential impact of this research on the broader cybersecurity landscape. In conclusion, the "Development of a Machine Learning-Based Intrusion Detection System for IoT Networks" represents a significant step towards addressing the security challenges faced by IoT environments. By leveraging machine learning techniques, this research offers a promising solution to enhance the detection and mitigation of intrusions in IoT networks, ultimately contributing to the advancement of cybersecurity in the IoT domain.

Thesis Overview

The project titled "Development of a Machine Learning-Based Intrusion Detection System for IoT Networks" aims to address the increasing security concerns associated with Internet of Things (IoT) networks. As IoT devices become more prevalent in various industries and households, the need for robust security measures to protect these interconnected devices from cyber threats has become paramount. Traditional security mechanisms are often insufficient to safeguard IoT networks due to the unique characteristics of IoT devices, such as limited computational resources and diverse communication protocols. The proposed project focuses on leveraging machine learning techniques to develop an advanced intrusion detection system tailored specifically for IoT environments. By harnessing the power of machine learning algorithms, the system will be able to analyze network traffic patterns, detect anomalies, and identify potential security breaches in real-time. This proactive approach to security will help mitigate the risks associated with unauthorized access, data breaches, and other malicious activities targeting IoT devices. The research will begin with a comprehensive literature review to explore the existing techniques and methodologies in intrusion detection systems, machine learning, and IoT security. By synthesizing and analyzing the latest research findings, the project aims to identify gaps in the current body of knowledge and propose innovative solutions to enhance the security of IoT networks. The methodology will involve designing and implementing a prototype intrusion detection system that integrates machine learning algorithms for anomaly detection. The system will be tested and evaluated using simulated IoT network environments to assess its performance in detecting and mitigating various types of cyber threats. The research will also consider practical aspects such as scalability, computational efficiency, and adaptability to different IoT architectures. The findings from this study are expected to contribute significantly to the field of cybersecurity, particularly in the context of IoT networks. By developing a machine learning-based intrusion detection system tailored for IoT environments, the project aims to enhance the overall security posture of IoT devices and networks, ultimately safeguarding critical assets and sensitive data from potential cyber attacks. In conclusion, the project "Development of a Machine Learning-Based Intrusion Detection System for IoT Networks" represents a significant step towards addressing the evolving security challenges in IoT ecosystems. By leveraging machine learning technologies, the research aims to provide a proactive and adaptive security solution that can effectively detect and respond to security threats in real-time, thereby ensuring the integrity and confidentiality of IoT networks in an increasingly interconnected world.