Thesis Abstract

Abstract
In recent years, the rapid growth of Internet of Things (IoT) networks has revolutionized various industries by enabling seamless connectivity and communication between devices. However, the interconnected nature of IoT networks also poses significant security challenges, particularly in detecting and mitigating anomalies that could potentially lead to security breaches and data compromises. This research project focuses on addressing these challenges by leveraging machine learning techniques for anomaly detection in IoT networks. The primary objective of this study is to develop and evaluate novel machine learning models capable of effectively detecting anomalies in IoT networks. The research methodology involves a comprehensive literature review to understand the existing approaches and methodologies in anomaly detection, particularly in the context of IoT networks. The study also includes the collection and analysis of real-world IoT network data to train and evaluate the proposed machine learning models. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter 2 presents a detailed literature review covering ten key aspects related to anomaly detection in IoT networks, including existing techniques, challenges, and emerging trends. Chapter 3 outlines the research methodology, which includes data collection, preprocessing, feature selection, model training, evaluation, and validation. The chapter also discusses the selection of appropriate machine learning algorithms and evaluation metrics for assessing the performance of the proposed models. Additionally, considerations for handling imbalanced datasets and ensuring model robustness are also addressed. Chapter 4 presents a comprehensive discussion of the findings obtained from the experimental evaluation of the developed machine learning models. The chapter analyzes the performance metrics, compares the results with existing approaches, and discusses the implications of the findings in the context of anomaly detection in IoT networks. Furthermore, potential areas for future research and improvements are also highlighted. Chapter 5 concludes the thesis by summarizing the key findings, contributions, and implications of the study. The chapter also provides recommendations for practitioners and policymakers to enhance the security and resilience of IoT networks through effective anomaly detection mechanisms. Overall, this research project aims to advance the field of anomaly detection in IoT networks by proposing innovative machine learning techniques and methodologies to address the evolving security challenges in interconnected IoT environments.

Thesis Overview