Thesis Abstract

Abstract
The rapid growth of Internet of Things (IoT) devices and networks has led to an increased need for robust security mechanisms to protect against potential cyber threats. Anomaly detection is a crucial component of cybersecurity, aiming to identify unusual patterns or behaviors that may indicate malicious activities within IoT networks. Machine learning algorithms have gained popularity in anomaly detection due to their ability to effectively analyze and identify patterns in large datasets. This thesis investigates the application of machine learning algorithms for anomaly detection in IoT networks. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for the research by highlighting the importance of anomaly detection in IoT networks and the role of machine learning algorithms in enhancing security. Chapter 2 presents a comprehensive literature review that examines existing research on anomaly detection in IoT networks and the utilization of machine learning algorithms for this purpose. The review covers various approaches, techniques, and tools used in anomaly detection, providing a critical analysis of their strengths and limitations. Chapter 3 details the research methodology employed in this study, including data collection methods, dataset preparation, feature selection, algorithm selection, model training, evaluation metrics, and validation techniques. The chapter also discusses the experimental setup and outlines the steps taken to ensure the reliability and validity of the results. Chapter 4 presents an in-depth discussion of the findings obtained from applying machine learning algorithms to detect anomalies in IoT networks. The chapter analyzes the performance of different algorithms, identifies key challenges, and proposes recommendations for improving the accuracy and efficiency of anomaly detection systems. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and highlighting potential areas for future work. The chapter emphasizes the significance of using machine learning algorithms for anomaly detection in IoT networks and underscores the importance of continuous research and development in this field to address evolving cybersecurity threats. Overall, this thesis contributes to the body of knowledge in the field of cybersecurity by exploring the effectiveness of machine learning algorithms for anomaly detection in IoT networks. The research findings provide valuable insights for researchers, practitioners, and policymakers involved in enhancing the security of IoT ecosystems and mitigating potential cyber risks.

Thesis Overview

The project titled "Anomaly Detection in IoT Networks Using Machine Learning Algorithms" focuses on addressing the critical challenge of detecting anomalies in Internet of Things (IoT) networks through the application of machine learning algorithms. The proliferation of IoT devices has led to massive amounts of data being generated, making it increasingly difficult to manually monitor and identify abnormal behavior within these networks. By leveraging machine learning techniques, this project aims to automate the process of anomaly detection in IoT networks, thereby enhancing network security and reliability. The research will begin with a comprehensive introduction that outlines the background of the study, the problem statement, research objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. This foundational chapter will lay the groundwork for understanding the importance and relevance of anomaly detection in IoT networks and the role of machine learning in improving network security. The literature review chapter will delve into existing research and methodologies related to anomaly detection in IoT networks and machine learning algorithms. This section will provide a thorough analysis of relevant studies, frameworks, and approaches that have been used to address similar challenges, highlighting gaps in the current literature that this project aims to fill. The research methodology chapter will detail the approach taken to implement anomaly detection using machine learning algorithms in IoT networks. This section will outline the data collection methods, data preprocessing techniques, selection of machine learning models, feature engineering, model training, evaluation metrics, and validation procedures to be employed in the study. The discussion of findings chapter will present the results of the experiments conducted to detect anomalies in IoT networks using machine learning algorithms. This section will analyze the performance of different machine learning models, evaluate the effectiveness of the proposed approach, and discuss the implications of the findings for improving anomaly detection in IoT networks. Finally, the conclusion and summary chapter will provide a comprehensive overview of the project, summarizing the key findings, discussing the implications of the research, and offering recommendations for future work in this area. This section will highlight the contributions of the study to the field of IoT network security and machine learning, emphasizing the importance of automated anomaly detection in ensuring the reliability and security of IoT networks. Overall, this research overview demonstrates the significance of the project in addressing the challenges of anomaly detection in IoT networks and highlights the potential impact of using machine learning algorithms to enhance network security and reliability.