Thesis Abstract

Abstract
The rapid growth of Internet of Things (IoT) devices has led to an increased need for effective anomaly detection methods to ensure the security and reliability of IoT networks. This research project focuses on applying machine learning techniques for anomaly detection in IoT networks. The primary aim is to develop a robust and efficient anomaly detection system that can accurately identify and classify abnormal behaviors in IoT devices. The thesis begins with a comprehensive introduction to the research topic, providing background information on IoT networks and the significance of anomaly detection in ensuring network security. The problem statement highlights the challenges and limitations faced in current anomaly detection methods, emphasizing the need for more advanced techniques to address these issues. The research objectives are outlined to guide the study towards achieving specific goals, while the scope and limitations of the research define the boundaries within which the study will be conducted. Chapter 2 presents a detailed literature review, covering a wide range of existing studies and approaches to anomaly detection in IoT networks. The review includes discussions on various machine learning algorithms, anomaly detection techniques, and their applications in IoT security. By examining the strengths and weaknesses of different methods, this chapter provides a solid foundation for the research methodology. Chapter 3 focuses on the research methodology employed to develop the anomaly detection system. The methodology includes data collection, preprocessing, feature selection, model training, and evaluation processes. Various machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks will be implemented and compared to identify the most effective approach for anomaly detection in IoT networks. In Chapter 4, the findings of the research are presented and discussed in detail. The performance of the developed anomaly detection system is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results of the experiments conducted on real-world IoT datasets demonstrate the effectiveness and efficiency of the proposed system in detecting anomalies and classifying them accurately. Finally, Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting areas for future work. The significance of the study in enhancing the security of IoT networks through advanced anomaly detection techniques is highlighted. Overall, this research contributes to the growing body of knowledge in IoT security and provides valuable insights into the application of machine learning for anomaly detection in IoT networks.

Thesis Overview