Thesis Abstract

Abstract
With the rapid increase in network traffic and the growing complexity of network systems, the need for effective anomaly detection techniques has become paramount. This thesis explores the application of machine learning algorithms for anomaly detection in network traffic. The primary objective is to develop a robust system that can detect anomalies in real-time, thereby enhancing network security and performance. The research begins with a comprehensive review of existing literature on anomaly detection, machine learning algorithms, and network traffic analysis. Various techniques and methodologies used in anomaly detection are discussed to provide a solid foundation for the study. The methodology chapter outlines the research design, data collection methods, and the selection of machine learning algorithms for anomaly detection. The research utilizes a dataset of network traffic logs for training and testing the machine learning models. The evaluation metrics used to assess the performance of the models are also discussed in this chapter. In the findings chapter, the results of the experiments conducted on the dataset are presented and analyzed. The performance of different machine learning algorithms in detecting anomalies in network traffic is compared, highlighting their strengths and limitations. The findings provide valuable insights into the effectiveness of various algorithms in detecting different types of network anomalies. The discussion chapter delves into the implications of the findings and their significance in the context of network security. The challenges encountered during the research process are also addressed, along with potential areas for future research and improvement. In conclusion, this thesis presents a novel approach to anomaly detection in network traffic using machine learning algorithms. The research contributes to the existing body of knowledge in the field of network security and provides practical insights for implementing effective anomaly detection systems in real-world networks. The findings of this study have the potential to enhance network security measures and improve the overall performance of network systems. Keywords Anomaly detection, Network traffic, Machine learning algorithms, Network security, Data analysis.

Thesis Overview

The project titled "Anomaly detection in network traffic using machine learning algorithms" aims to address the critical issue of detecting anomalies in network traffic data by leveraging the power of machine learning algorithms. With the increasing complexity and volume of network traffic data, traditional methods of anomaly detection are becoming insufficient to effectively identify and respond to abnormalities in real-time. This research proposes a novel approach that harnesses the capabilities of machine learning to enhance the accuracy and efficiency of anomaly detection in network traffic. The research will begin by providing a comprehensive introduction to the importance of anomaly detection in network traffic and the challenges associated with traditional methods. It will delve into the background of the study, exploring existing literature and technologies related to anomaly detection and machine learning algorithms in the context of network security. The problem statement will clearly define the gaps in current approaches and highlight the need for a more robust and intelligent solution. The objectives of the study will be outlined to establish the specific goals and outcomes that the research aims to achieve. These objectives will guide the development and evaluation of the proposed anomaly detection system. The limitations and scope of the study will be clearly defined to set realistic boundaries and expectations for the research. The significance of the study will be emphasized, highlighting the potential impact of improving anomaly detection in network traffic on cybersecurity, network performance, and overall system reliability. By enhancing the ability to detect and respond to anomalies in real-time, organizations can better protect their networks and data from potential threats and vulnerabilities. The structure of the thesis will be detailed to provide a roadmap for the reader, outlining the organization and flow of the research document. This will help to guide the audience through the various sections and chapters of the thesis, ensuring a clear and logical progression of ideas and findings. Overall, this research overview sets the stage for an in-depth exploration of anomaly detection in network traffic using machine learning algorithms. By combining advanced data analytics with intelligent algorithms, this project aims to enhance the efficiency and effectiveness of anomaly detection systems, ultimately contributing to the advancement of network security and data protection in the digital age.