Thesis Abstract

Abstract
In the modern digital era, with the continuous growth of network traffic and the increasing complexity of network systems, the need for effective anomaly detection techniques has become paramount. Anomaly detection plays a crucial role in ensuring the security and stability of network infrastructures by identifying unusual patterns or behaviors that may indicate malicious activities or system failures. This thesis focuses on the application of machine learning algorithms for anomaly detection in network traffic, aiming to enhance the accuracy and efficiency of detecting abnormal network behavior. The research begins with a comprehensive literature review to explore existing methodologies and approaches to anomaly detection in network traffic. Various machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks are examined in terms of their applicability and effectiveness in detecting anomalies in network data. Subsequently, the research methodology chapter details the process of data collection, preprocessing, feature selection, and model training for anomaly detection. The study utilizes a real-world network traffic dataset to evaluate the performance of different machine learning algorithms in detecting anomalies accurately and efficiently. The findings chapter presents a detailed analysis of the experimental results, comparing the performance of various machine learning algorithms in terms of detection accuracy, false positive rate, and computational efficiency. The discussion delves into the strengths and limitations of each algorithm, highlighting their potential for practical implementation in real-world network environments. In conclusion, this research contributes to the field of anomaly detection in network traffic by demonstrating the effectiveness of machine learning algorithms in identifying abnormal patterns and behaviors. The study provides valuable insights into the application of advanced computational techniques for enhancing network security and reliability. The findings of this thesis have implications for network administrators, cybersecurity professionals, and researchers working in the field of network security. Overall, this thesis underscores the importance of leveraging machine learning algorithms for anomaly detection in network traffic to mitigate security risks, enhance system performance, and ensure the integrity of network infrastructures in the face of evolving cyber threats.

Thesis Overview

The project titled "Anomaly Detection in Network Traffic Using Machine Learning Algorithms" focuses on the application of machine learning techniques to detect anomalies in network traffic data. With the increasing complexity and volume of network data, traditional methods of detecting anomalies have become inadequate, necessitating the adoption of more advanced techniques such as machine learning. The primary objective of this research is to develop a robust anomaly detection system that can effectively identify unusual patterns or behaviors in network traffic data. By leveraging the power of machine learning algorithms, the study aims to enhance the accuracy and efficiency of anomaly detection, thereby improving network security and performance. The research will begin with a comprehensive review of existing literature on anomaly detection, machine learning algorithms, and network traffic analysis. This literature review will provide a solid theoretical foundation for the study and help identify gaps in current research that can be addressed through the proposed project. The methodology chapter will outline the research approach, data collection methods, feature selection techniques, and the machine learning algorithms to be employed in the anomaly detection system. The study will utilize a diverse dataset of network traffic data to train and test the machine learning models, ensuring the robustness and generalizability of the proposed system. The findings chapter will present the results of the experimental evaluation of the anomaly detection system. Performance metrics such as accuracy, precision, recall, and F1-score will be used to assess the effectiveness of the machine learning algorithms in detecting anomalies in network traffic data. The discussion will delve into the strengths and limitations of the proposed system, as well as potential areas for future research and improvement. In conclusion, this research aims to contribute to the field of network security by developing an advanced anomaly detection system that leverages machine learning algorithms to effectively identify and mitigate threats in network traffic data. The study holds the potential to enhance the resilience of network infrastructures and improve overall cybersecurity measures in a rapidly evolving digital landscape.