Thesis Abstract

Abstract
Anomaly detection in network traffic plays a crucial role in ensuring the security and stability of computer networks. With the increasing complexity and volume of network data, traditional rule-based methods for anomaly detection have become less effective. This thesis focuses on utilizing machine learning techniques to enhance the accuracy and efficiency of anomaly detection in network traffic. The primary objective of this study is to develop a novel machine learning-based approach that can effectively identify anomalies in network traffic data. Chapter 1 provides an introduction to the research topic, including 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 study by highlighting the importance of anomaly detection in network traffic and the potential benefits of using machine learning techniques. Chapter 2 presents a comprehensive literature review on anomaly detection methods in network traffic. The review covers ten key areas, including traditional rule-based approaches, machine learning algorithms, deep learning techniques, feature selection methods, and evaluation metrics used in anomaly detection research. This chapter provides a detailed overview of existing research in the field and identifies gaps that the current study aims to address. Chapter 3 details the research methodology employed in this study. It includes the data collection process, preprocessing steps, feature engineering techniques, selection of machine learning algorithms, model training, evaluation methods, and performance metrics used to assess the effectiveness of the proposed approach. The chapter also discusses the experimental setup and validation procedures followed to ensure the reliability of the results. Chapter 4 presents a thorough discussion of the findings obtained from the experimental evaluation of the proposed anomaly detection approach. The chapter analyzes the performance of the machine learning models in detecting anomalies in network traffic data and compares the results with existing methods. It also discusses the impact of different factors, such as feature selection and algorithm parameters, on the performance of the models. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and highlighting future research directions in the field of anomaly detection in network traffic using machine learning. The chapter also provides recommendations for practitioners and researchers interested in implementing machine learning-based anomaly detection systems in real-world network environments. In conclusion, this thesis contributes to the advancement of anomaly detection techniques in network traffic through the development and evaluation of a novel machine learning-based approach. The findings of this study provide valuable insights into improving the accuracy and efficiency of anomaly detection systems, ultimately enhancing the security and reliability of computer networks.

Thesis Overview