Anomaly Detection in Network Traffic Using Machine Learning Algorithms

Thesis Abstract

Abstract
The rapid growth of network communication technologies has led to an increase in the complexity and volume of network traffic data. As a result, the detection of anomalies in network traffic has become a critical task for ensuring the security and reliability of network systems. Traditional rule-based methods for anomaly detection have limitations in detecting unknown or evolving network threats. In contrast, machine learning algorithms have shown promising results in identifying anomalous patterns in network traffic data. This thesis focuses on the application of machine learning algorithms for anomaly detection in network traffic data. Chapter 1 provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance of the study, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to anomaly detection, machine learning algorithms, and network traffic analysis. The literature review provides a theoretical foundation for understanding the research problem and existing approaches in the field. Chapter 3 outlines the research methodology used in this study, including data collection, preprocessing, feature selection, model training, evaluation metrics, and validation techniques. The methodology section discusses the selection of appropriate machine learning algorithms for anomaly detection in network traffic data and the experimental setup for evaluating the performance of the models. Chapter 4 presents a detailed discussion of the findings obtained from the experiments conducted in this study. The results of the experiments are analyzed, and the performance of different machine learning algorithms in detecting anomalies in network traffic data is evaluated. The discussion section highlights the strengths and limitations of the proposed approach and provides insights into future research directions in the field of anomaly detection. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research results, and providing recommendations for future work. The conclusion also reflects on the significance of using machine learning algorithms for anomaly detection in network traffic data and the potential impact of this research on enhancing network security and performance. Overall, this thesis contributes to the advancement of anomaly detection techniques in network traffic analysis by leveraging machine learning algorithms to improve the detection of abnormal patterns and potential security threats. The research outcomes highlight the effectiveness of machine learning approaches in enhancing the accuracy and efficiency of anomaly detection systems, paving the way for more robust and adaptive network security solutions in the future.