Anomaly Detection in Network Traffic Using Machine Learning Algorithms

Project Abstract

The rapid growth of network technologies has led to an increase in the volume and complexity of network traffic data. As a result, the need for effective anomaly detection techniques to identify potential threats and abnormal behavior within networks has become crucial to ensure the security and integrity of systems. In this research study, we focus on applying machine learning algorithms for anomaly detection in network traffic data. The primary objective of this research is to develop and evaluate machine learning models for detecting anomalies in network traffic data. The study begins with a comprehensive review of existing literature on anomaly detection techniques, machine learning algorithms, and their application in network security. This literature review provides a foundation for understanding the current state of the art and identifying gaps in research that this study aims to address. The research methodology involves collecting and preprocessing network traffic data from various sources to create a suitable dataset for training and testing machine learning models. Several machine learning algorithms, including but not limited to support vector machines, decision trees, and neural networks, will be implemented and evaluated for their performance in detecting anomalies in the network traffic data. Chapter four presents a detailed discussion of the experimental results and findings obtained from applying the machine learning algorithms to the network traffic dataset. The evaluation metrics used to assess the performance of the models include accuracy, precision, recall, and F1-score. The findings of the study will provide insights into the effectiveness of different machine learning algorithms for anomaly detection in network traffic data. In conclusion, this research contributes to the field of network security by exploring the use of machine learning algorithms for anomaly detection in network traffic. The study highlights the potential of machine learning techniques to enhance the detection of suspicious and malicious activities within networks. The findings of this research can be valuable for security analysts, network administrators, and researchers working in the field of cybersecurity. Overall, this research project aims to advance the field of anomaly detection in network traffic using machine learning algorithms and provides a foundation for further research in this important area of network security.

Project Overview