Project Abstract

Abstract
Anomaly detection in network traffic plays a crucial role in ensuring the security and efficient operation of computer networks. With the increasing complexity and volume of network data, traditional rule-based methods for anomaly detection are no longer sufficient. Machine learning algorithms have emerged as powerful tools for identifying anomalous patterns in network traffic data. This research project focuses on exploring the application of machine learning algorithms for anomaly detection in network traffic. The research begins with a comprehensive introduction that outlines the significance of anomaly detection in network traffic and the limitations of existing methods. The background of the study provides an overview of the current state of anomaly detection techniques and the challenges faced in detecting network anomalies. The problem statement highlights the need for more advanced and accurate anomaly detection methods to address the evolving nature of network threats. The objectives of the study are to develop machine learning models that can effectively identify anomalies in network traffic data and to evaluate their performance in real-world scenarios. The scope of the study encompasses the collection and analysis of network traffic data from diverse sources to train and test the machine learning models. The limitations of the study include the availability of labeled training data and the computational resources required for training complex machine learning models. The significance of the study lies in its potential to enhance the security and performance of computer networks by enabling early detection of anomalous behavior. By leveraging machine learning algorithms, network administrators can proactively identify and respond to security threats, minimizing the impact of cyber attacks and improving network efficiency. The structure of the research is divided into five chapters. Chapter One provides an overview of the research, including the introduction, background of the study, problem statement, objectives, limitations, scope, significance, and the structure of the research. Chapter Two comprises a comprehensive literature review that explores existing research on anomaly detection in network traffic and the application of machine learning algorithms in this context. Chapter Three details the research methodology, including data collection, preprocessing, feature selection, model training, evaluation metrics, and experimental setup. The chapter also discusses the selection of machine learning algorithms and the parameters used for training the models. Chapter Four presents the findings of the research, including the performance evaluation of the developed machine learning models in detecting anomalies in network traffic data. The chapter includes a detailed discussion of the results, highlighting the strengths and limitations of the models and potential areas for improvement. Chapter Five concludes the research with a summary of the key findings, implications of the study, and recommendations for future research. The chapter also discusses the practical applications of the developed machine learning models in enhancing network security and performance. Overall, this research project aims to contribute to the field of anomaly detection in network traffic by developing and evaluating machine learning algorithms that can effectively detect anomalies in real-world network environments. Through this research, network administrators can enhance the security and efficiency of computer networks, mitigating the risks associated with network anomalies and cyber threats.

Project Overview

"Anomaly Detection in Network Traffic Using Machine Learning Algorithms"