Thesis Abstract

Anomaly detection in network traffic is a critical area of research in the field of computer science, with the increasing complexity and volume of network data. Traditional methods of detecting anomalies in network traffic are becoming less effective in addressing the evolving nature of cyber threats. This thesis focuses on leveraging machine learning techniques to enhance the accuracy and efficiency of anomaly detection in network traffic. The abstract begins with an overview of the challenges associated with traditional anomaly detection methods in network traffic analysis. It highlights the limitations of rule-based approaches and the need for more sophisticated techniques to detect both known and unknown anomalies effectively. The study provides a comprehensive review of the existing literature on anomaly detection in network traffic, emphasizing the role of machine learning algorithms in enhancing detection accuracy. Various machine learning models, such as neural networks, support vector machines, and random forests, are explored for their potential in network anomaly detection. The research methodology section outlines the process of collecting and preprocessing network traffic data for model training and evaluation. The study evaluates the performance of different machine learning algorithms in detecting anomalies in network traffic datasets, considering factors such as detection accuracy, false positive rate, and computational efficiency. The findings from the experiments are discussed in detail, highlighting the strengths and limitations of each machine learning algorithm in detecting anomalies in network traffic. The study also explores the impact of different feature selection techniques and hyperparameter tuning on the performance of the models. In conclusion, the study emphasizes the importance of utilizing machine learning techniques for anomaly detection in network traffic to enhance cybersecurity measures. The thesis contributes to the existing body of knowledge by providing insights into the effectiveness of various machine learning algorithms in detecting anomalies in network traffic data. The findings of this research have practical implications for improving the security of network systems and mitigating cyber threats effectively.

Thesis Overview