Anomaly Detection in Network Traffic Using Machine Learning Techniques

Thesis Abstract

Abstract
The increasing complexity and volume of network traffic have made it challenging for organizations to effectively monitor and detect anomalies in their network systems. Traditional rule-based approaches are no longer sufficient to detect sophisticated and evolving network threats. This research project focuses on utilizing machine learning techniques for anomaly detection in network traffic. The objective is to develop a robust and accurate anomaly detection system that can detect both known and unknown network anomalies in real-time. The study begins with an introduction to the problem statement, highlighting the limitations of current anomaly detection methods and the need for more advanced techniques. The background of the study provides an overview of network traffic analysis and the importance of anomaly detection in maintaining the security and integrity of network systems. The objectives of the study are outlined to guide the research process, while the limitations and scope of the study define the boundaries and constraints within which the research is conducted. A comprehensive literature review is presented in Chapter Two, covering ten key areas related to anomaly detection, machine learning, network security, and existing approaches in the field. This review provides a foundation for understanding the current state of research in anomaly detection and identifies gaps that this study aims to address. Chapter Three details the research methodology employed in this study, including data collection, preprocessing, feature extraction, model selection, and evaluation metrics. The methodology section also describes the datasets used for training and testing the anomaly detection models, as well as the experimental setup and performance evaluation criteria. In Chapter Four, the findings of the study are discussed in detail, including the performance of different machine learning algorithms for anomaly detection, the detection accuracy, false positive rates, and computational efficiency. The results are analyzed and compared to existing approaches to highlight the strengths and limitations of the proposed anomaly detection system. Finally, Chapter Five provides a summary of the research findings, conclusions drawn from the study, and recommendations for future research directions. The significance of the study is discussed in terms of its contributions to the field of network security and the potential impact on improving anomaly detection capabilities in real-world network environments. Overall, this thesis contributes to the advancement of anomaly detection in network traffic using machine learning techniques and provides valuable insights into enhancing network security measures in the face of evolving cyber threats.