Thesis Abstract

Abstract
The rapid growth of network traffic in modern computer systems has increased the complexity of detecting anomalies and potential security threats. Anomaly detection plays a crucial role in maintaining the integrity and security of networks, as it helps in identifying unusual patterns that may indicate malicious activities or system failures. Machine learning algorithms have shown promising results in automating the detection of anomalies in network traffic, offering a proactive approach to network security. This thesis focuses on the application of machine learning algorithms for anomaly detection in network traffic. The study begins with an introduction to the significance of anomaly detection in network security and the challenges associated with traditional rule-based methods. A comprehensive literature review is conducted to explore the existing research on anomaly detection techniques, highlighting the strengths and limitations of different machine learning algorithms in this context. The research methodology section outlines the approach taken to develop and evaluate the proposed anomaly detection system. It describes the dataset used for training and testing the machine learning models, as well as the evaluation metrics employed to assess the performance of the system. The methodology also covers the preprocessing steps involved in preparing the network traffic data for analysis and model training. The findings of the study are presented and discussed in detail in Chapter 4. The performance of various machine learning algorithms, such as support vector machines, random forests, and neural networks, in detecting anomalies in network traffic is evaluated and compared. The results reveal the strengths and weaknesses of each algorithm in terms of accuracy, speed, and scalability, providing valuable insights for network security practitioners and researchers. In conclusion, this thesis offers a comprehensive examination of the application of machine learning algorithms for anomaly detection in network traffic. The findings demonstrate the potential of machine learning in enhancing the efficiency and effectiveness of anomaly detection systems, paving the way for future advancements in network security. The study contributes to the growing body of knowledge on network security and provides practical recommendations for implementing machine learning-based anomaly detection solutions in real-world settings.

Thesis Overview