Project Abstract

Abstract
The rapid growth of network traffic in modern computer networks has led to an increase in security threats and attacks. Anomaly detection is a crucial aspect of network security that aims to identify unusual or suspicious patterns that may indicate a security breach. This research project focuses on the application of machine learning techniques for anomaly detection in network traffic. The main objective is to develop an effective system that can accurately detect and classify network anomalies in real-time. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definition of key terms. The literature review in Chapter 2 explores existing research on anomaly detection in network traffic, covering topics such as different machine learning algorithms, datasets, evaluation metrics, and comparative studies. Chapter 3 details the research methodology, including data collection, preprocessing, feature selection, model training, evaluation, and validation. Various machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks will be implemented and compared for their performance in detecting network anomalies. The research methodology also includes the selection of appropriate datasets, feature engineering techniques, and evaluation metrics to assess the effectiveness of the proposed anomaly detection system. In Chapter 4, the findings of the research are discussed in detail, including the performance evaluation of different machine learning models in detecting network anomalies. The results will be analyzed based on metrics such as accuracy, precision, recall, and F1 score. The discussion will also cover the strengths and limitations of the proposed system, as well as potential areas for future research and improvement. Finally, Chapter 5 presents the conclusion and summary of the research project. The key findings, contributions, and implications of the study are summarized, along with recommendations for further research in the field of anomaly detection in network traffic using machine learning techniques. Overall, this research aims to contribute to the development of more robust and efficient systems for enhancing network security through advanced anomaly detection methods.

Project Overview