Anomaly Detection in Network Traffic Using Machine Learning Techniques

Thesis Abstract

Abstract
The continuous growth of network traffic volume and complexity has made anomaly detection in network traffic a crucial task in ensuring the security and integrity of network systems. Machine learning techniques have emerged as powerful tools for detecting anomalies in network traffic data due to their ability to learn patterns and detect deviations from normal behavior. This thesis focuses on the application of machine learning techniques for anomaly detection in network traffic and aims to provide a comprehensive analysis of their effectiveness in detecting various types of anomalies. Chapter 1 provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The literature review in Chapter 2 covers ten key studies related to anomaly detection in network traffic using machine learning techniques. Chapter 3 details the research methodology, including data collection, preprocessing, feature selection, model training, evaluation metrics, and experimental setup. In Chapter 4, the findings of the study are discussed in detail, including the performance of different machine learning algorithms in detecting anomalies in network traffic data. The chapter also explores the impact of various factors such as feature selection, model hyperparameters, and data imbalance on the detection accuracy. The results are analyzed and compared with existing literature to highlight the strengths and limitations of the proposed approach. Finally, Chapter 5 presents the conclusion and summary of the thesis, discussing the key findings, contributions, and implications of the study. The research highlights the importance of leveraging machine learning techniques for anomaly detection in network traffic and provides valuable insights for improving the accuracy and efficiency of anomaly detection systems in real-world network environments. This thesis contributes to the growing body of knowledge in the field of network security and lays the foundation for future research in the area of anomaly detection using machine learning techniques.