Project Abstract

Abstract
With the increasing complexity and volume of network traffic data, the need for effective anomaly detection techniques has become crucial in ensuring the security and integrity of computer networks. This research project focuses on the application of machine learning algorithms for the detection of anomalies in network traffic. The study aims to develop a robust system that can accurately identify unusual patterns and potential security threats in network data. The research begins with a comprehensive review of existing literature on anomaly detection methods in network traffic analysis. Various machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks will be explored to determine their effectiveness in detecting anomalies in network traffic data. The study will also investigate the impact of different feature selection techniques on the performance of these algorithms. The research methodology involves collecting and preprocessing network traffic data from various sources to build a labeled dataset for training and testing the machine learning models. The selected algorithms will be implemented and evaluated based on their detection accuracy, false positive rate, and computational efficiency. Furthermore, the study will explore the interpretability of the models and their ability to adapt to changing network environments. The findings of this research will be presented and discussed in detail in Chapter Four, highlighting the performance of different machine learning algorithms in detecting anomalies in network traffic. The results will be compared and analyzed to identify the strengths and limitations of each algorithm in this context. Additionally, the research will investigate the impact of feature selection techniques on the overall performance of the anomaly detection system. In conclusion, this research project aims to contribute to the field of network security by developing an effective anomaly detection system using machine learning algorithms. The study will provide insights into the performance of different algorithms and feature selection techniques in detecting anomalies in network traffic data. The findings of this research will be valuable for network administrators and security analysts in enhancing the security posture of computer networks.

Project Overview