Anomaly Detection in Network Traffic Using Machine Learning Techniques

Thesis Abstract

Abstract
Anomaly detection in network traffic using machine learning techniques is a critical area of research in computer science and cybersecurity. As the volume and complexity of network data continue to grow, the ability to accurately detect and classify anomalies in network traffic is essential for maintaining the security and integrity of computer networks. This thesis presents a comprehensive study on the application of machine learning algorithms for anomaly detection in network traffic. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance and relevance of anomaly detection in network traffic. Chapter 2 is dedicated to a detailed literature review, covering ten key aspects related to anomaly detection in network traffic. The review includes discussions on existing methodologies, techniques, and tools used for anomaly detection in network traffic, highlighting the strengths and limitations of each approach. Chapter 3 outlines the research methodology employed in this study, describing the data collection process, preprocessing techniques, feature selection methods, and the machine learning algorithms utilized for anomaly detection. The chapter also discusses the evaluation metrics and procedures used to assess the performance of the anomaly detection models. In Chapter 4, the findings of the research are presented and discussed in detail. The chapter includes an in-depth analysis of the performance of different machine learning algorithms for anomaly detection in network traffic. The results are compared, and the effectiveness of the proposed methodologies is evaluated based on various metrics. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future work in the field of anomaly detection in network traffic using machine learning techniques. The chapter also highlights the contributions of this study to the existing body of knowledge and suggests avenues for further research. In conclusion, this thesis contributes to the advancement of anomaly detection techniques in network traffic by leveraging machine learning algorithms. The research findings provide valuable insights into the effectiveness of different approaches for detecting anomalies in network traffic data, with implications for enhancing the security and performance of computer networks.