Machine Learning Techniques for Anomaly Detection in Network Security

Thesis Abstract

Abstract
Network security is a critical aspect of modern information technology systems, with the increasing number of cyber threats and attacks targeting network infrastructures. Anomaly detection plays a crucial role in identifying unusual activities that may indicate potential security breaches within a network. Machine learning techniques have shown significant promise in enhancing anomaly detection capabilities by enabling automated and intelligent analysis of network traffic patterns. This thesis focuses on exploring various machine learning techniques for anomaly detection in network security. The research begins with a comprehensive review of existing literature to establish the theoretical foundation and identify gaps in current approaches. The methodology involves the collection and preprocessing of network traffic data, feature extraction, model training, and evaluation using a variety of machine learning algorithms. Chapter 1 provides an introduction to the research topic, background information on network security, a problem statement highlighting the importance of anomaly detection, research objectives, limitations, scope, significance, the structure of the thesis, and definitions of key terms. Chapter 2 presents a detailed literature review covering ten key aspects related to machine learning techniques and anomaly detection in network security. In Chapter 3, the research methodology is outlined, including data collection methods, data preprocessing techniques, feature extraction approaches, model selection, training, and evaluation methodologies. The chapter also discusses the metrics used to evaluate the performance of the machine learning models and the experimental setup. Chapter 4 presents a comprehensive discussion of the findings obtained from the experiments conducted as part of this research. The results are analyzed, and the performance of different machine learning algorithms in detecting anomalies in network traffic is compared and discussed in detail. The challenges encountered during the research process are also addressed, along with potential solutions and future research directions. Finally, Chapter 5 summarizes the key findings of the research, discusses the implications of the results, and provides recommendations for further research in this field. The conclusion highlights the significance of machine learning techniques in enhancing anomaly detection in network security and emphasizes the importance of continuous research and development in this area to stay ahead of evolving cyber threats. This thesis contributes to the field of network security by demonstrating the effectiveness of machine learning techniques for anomaly detection and providing insights into improving the overall security posture of network infrastructures. The findings of this research can be leveraged by cybersecurity professionals, network administrators, and researchers to enhance the security measures and protect against potential cyber threats in an increasingly interconnected digital environment.