Project Abstract

Abstract
Network traffic anomaly detection is a critical aspect of ensuring the security and reliability of computer networks. With the increasing complexity and volume of network data, traditional rule-based methods are no longer sufficient to detect anomalies effectively. This research project focuses on utilizing machine learning algorithms for anomaly detection in network traffic. The objective is to develop a robust and accurate anomaly detection system that can adapt to changing network environments and effectively identify potential security threats. The research begins with a comprehensive literature review on existing methods and approaches for anomaly detection in network traffic. This review covers various machine learning algorithms commonly used in anomaly detection, such as support vector machines, random forests, and deep learning models. The review also discusses the challenges and limitations of current approaches, highlighting the need for more advanced and adaptive anomaly detection systems. The research methodology involves collecting and preprocessing network traffic data from a variety of sources, including network logs, packet captures, and flow data. Feature engineering techniques are applied to extract relevant information from the raw data and create input features for the machine learning models. A variety of machine learning algorithms are implemented and evaluated for their performance in detecting anomalies in network traffic. The findings of the research shed light on the effectiveness of different machine learning algorithms in detecting network traffic anomalies. Results show that certain algorithms, such as deep learning models, outperform traditional methods in terms of accuracy and efficiency. The research also identifies key factors that influence the performance of anomaly detection systems, such as the choice of features, model hyperparameters, and training data size. The discussion of findings delves into the implications of the research results for network security professionals and system administrators. It highlights the potential benefits of using machine learning algorithms for anomaly detection, including improved detection rates, reduced false positives, and faster response times to security incidents. The discussion also addresses the challenges and limitations of implementing machine learning-based anomaly detection systems in real-world network environments. In conclusion, this research project demonstrates the feasibility and effectiveness of using machine learning algorithms for anomaly detection in network traffic. By leveraging the power of machine learning, organizations can enhance their network security posture and better protect their critical assets from cyber threats. The findings and insights from this research contribute to the ongoing efforts to develop more advanced and adaptive anomaly detection systems for securing modern computer networks.

Project Overview