Anomaly Detection in Network Traffic Using Machine Learning Algorithms

Thesis Abstract

Abstract
Anomaly detection in network traffic is a critical aspect of ensuring the security and integrity of computer networks. With the increasing complexity and volume of network data, traditional rule-based methods are becoming less effective in detecting anomalies. This research focuses on utilizing machine learning algorithms to enhance the accuracy and efficiency of anomaly detection in network traffic. The main objective of this study is to develop a robust anomaly detection system that can effectively identify and classify various types of anomalies in network traffic. The thesis begins with an introduction that provides an overview of the research problem and the significance of the study. The background of the study discusses the current state of anomaly detection in network traffic and highlights the limitations of existing methods. The problem statement identifies the challenges faced in detecting anomalies in network traffic, such as the increasing volume of data and the evolving nature of cyber threats. The objectives of the study include developing machine learning models for anomaly detection, evaluating the performance of these models, and comparing them with traditional methods. The limitations of the study are also discussed, such as the availability of labeled data for training the machine learning models and the potential bias in the dataset. The scope of the study outlines the specific focus areas and the extent to which the research will be conducted. The significance of the study lies in its potential to improve the accuracy and efficiency of anomaly detection in network traffic, thereby enhancing the overall security of computer networks. The structure of the thesis is outlined to provide a roadmap for the reader, detailing the chapters and their contents. Finally, the definition of terms clarifies key concepts and terminology used throughout the thesis. The literature review chapter explores existing research on anomaly detection in network traffic, highlighting the different approaches and algorithms used in previous studies. Key topics covered include machine learning algorithms, network security, anomaly detection techniques, and relevant datasets. The chapter aims to provide a comprehensive overview of the current state of the art in anomaly detection in network traffic. The research methodology chapter outlines the approach taken to develop and evaluate the machine learning models for anomaly detection. The contents include data collection, data preprocessing, feature selection, model training, evaluation metrics, and model comparison. The chapter also discusses the experimental setup and the validation process used to assess the performance of the machine learning models. The discussion of findings chapter presents the results of the experiments conducted to evaluate the performance of the machine learning models. The contents include the accuracy, precision, recall, and F1-score of the models, as well as a comparison with traditional methods. The chapter also discusses the strengths and limitations of the models and provides insights into potential areas for improvement. In conclusion, this thesis provides a comprehensive analysis of anomaly detection in network traffic using machine learning algorithms. The study demonstrates the effectiveness of machine learning models in detecting anomalies and highlights the benefits of leveraging advanced algorithms for network security. The findings of this research contribute to the ongoing efforts to enhance the security and resilience of computer networks against evolving cyber threats.