Thesis Abstract

Abstract
Anomaly detection in network traffic using machine learning algorithms is a critical area of research in the field of computer science and cybersecurity. This thesis presents a comprehensive study on the application of machine learning techniques to detect anomalies in network traffic, with the aim of improving the overall security and performance of computer networks. The increasing complexity and volume of network data make traditional rule-based methods insufficient for detecting sophisticated cyber threats. Machine learning algorithms offer a promising solution to address this challenge by automatically learning patterns and anomalies in network traffic data. The thesis begins with an introduction to the research problem, highlighting the importance of anomaly detection in ensuring the security and reliability of computer networks. The background of the study provides an overview of existing techniques and approaches used in anomaly detection, emphasizing the limitations of rule-based methods and the need for more advanced solutions. The problem statement defines the specific challenges and goals of the research, focusing on the development of effective machine learning models for detecting network anomalies. The objectives of the study include evaluating different machine learning algorithms for anomaly detection, comparing their performance, and identifying the most suitable approach for detecting anomalies in network traffic. The limitations of the study are also discussed, including constraints in data collection, model training, and evaluation. The scope of the study outlines the specific aspects of network traffic that will be considered, such as packet headers, payload data, and traffic patterns. The significance of the study lies in its potential to enhance the cybersecurity posture of organizations by enabling early detection and mitigation of network threats. By leveraging machine learning algorithms, network administrators can proactively identify suspicious activities, prevent security breaches, and maintain the integrity of their networks. The structure of the thesis is presented, outlining the organization of chapters and key sections for a comprehensive understanding of the research findings. Chapter two provides a detailed literature review of existing studies and research works related to anomaly detection in network traffic. The review covers a range of machine learning algorithms, anomaly detection techniques, and datasets used in previous studies, highlighting their strengths and weaknesses. Chapter three presents the research methodology, including data collection, preprocessing, feature extraction, model selection, training, and evaluation. The methodology outlines the steps taken to implement machine learning models for detecting anomalies in network traffic. Chapter four offers an elaborate discussion of the findings obtained from the experimental evaluation of machine learning algorithms for anomaly detection. The results are analyzed, compared, and interpreted to determine the effectiveness of different approaches in detecting network anomalies. The discussion also addresses the challenges faced during the research process and potential areas for future improvement. In conclusion, this thesis summarizes the key findings, contributions, and implications of the research on anomaly detection in network traffic using machine learning algorithms. The study demonstrates the feasibility and effectiveness of machine learning techniques in enhancing network security and detecting sophisticated threats. By leveraging the power of machine learning, organizations can strengthen their defense mechanisms and safeguard their networks against emerging cyber threats.

Thesis Overview