Thesis Abstract

Anomaly detection in network traffic is a critical aspect of cybersecurity, as it involves identifying unusual patterns or behaviors that deviate from normal network activity. With the increasing complexity and sophistication of cyber threats, traditional rule-based detection methods are often insufficient to detect emerging anomalies. This research aims to explore the application of machine learning algorithms for anomaly detection in network traffic, leveraging their ability to adapt and learn from data to detect novel threats. The thesis begins with an introduction that outlines the importance of anomaly detection in network security and provides an overview of the research objectives. The background of the study discusses the current state of anomaly detection techniques and the limitations of existing approaches in addressing modern cybersecurity challenges. The problem statement highlights the need for more advanced and adaptive methods to detect anomalies in network traffic effectively. The objectives of the study include developing and implementing machine learning algorithms for anomaly detection, evaluating their performance on real-world network datasets, and comparing them with traditional methods. The limitations of the study acknowledge potential constraints such as data availability, computational resources, and algorithm complexity. The scope of the study defines the boundaries within which the research will be conducted, focusing on specific types of network anomalies and machine learning techniques. The significance of the study lies in its potential to enhance network security by improving the detection of anomalies that may indicate malicious activities. By leveraging machine learning algorithms, this research aims to provide more accurate and timely detection of threats, reducing the risk of data breaches and cyber attacks. The structure of the thesis outlines the organization of the research, including chapters on literature review, research methodology, findings discussion, and conclusion. The literature review chapter presents a comprehensive review of existing research on anomaly detection in network traffic, covering various machine learning approaches and their applications in cybersecurity. The research methodology chapter describes the data collection process, feature selection, model training and evaluation, and performance metrics used to assess the effectiveness of the machine learning algorithms. The findings discussion chapter presents the results of the experiments conducted to evaluate the performance of the machine learning algorithms in detecting network anomalies. It analyzes the strengths and weaknesses of different algorithms, identifies key factors influencing detection accuracy, and discusses potential areas for improvement. The conclusion and summary chapter summarizes the research findings, highlights the contributions of the study, and provides recommendations for future research in this field. In conclusion, this thesis explores the application of machine learning algorithms for anomaly detection in network traffic, demonstrating their potential to enhance cybersecurity defenses. By leveraging advanced data analytics techniques, this research aims to improve the accuracy and efficiency of anomaly detection systems, ultimately contributing to the protection of critical network infrastructure against emerging cyber threats.

Thesis Overview