Anomaly Detection in Network Traffic Using Machine Learning Techniques

Thesis Abstract

Abstract
Anomaly detection in network traffic is a critical aspect of cybersecurity, as it helps in identifying and mitigating potential threats and attacks in real-time. This thesis focuses on the application of machine learning techniques for anomaly detection in network traffic. The study aims to develop and evaluate a robust anomaly detection system that can effectively detect and classify anomalies in network traffic data. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to anomaly detection in network traffic using machine learning techniques. Chapter 3 discusses the research methodology, detailing the data collection process, preprocessing techniques, feature selection, model selection, evaluation metrics, and experimental setup. The findings of the study are presented in Chapter 4, where the performance of different machine learning algorithms for anomaly detection in network traffic is evaluated and compared. The results demonstrate the effectiveness of the proposed anomaly detection system in accurately identifying and classifying anomalies in network traffic data. The discussion also highlights the strengths and limitations of the system, as well as potential areas for future research and improvement. In Chapter 5, the conclusion and summary of the thesis are provided, summarizing the key findings, contributions, and implications of the study. The research contributes to the field of cybersecurity by providing a practical and effective approach to anomaly detection in network traffic using machine learning techniques. The thesis concludes with recommendations for further research and development in the field of anomaly detection in network traffic for enhanced cybersecurity measures.