Thesis Abstract

The abstract for the thesis on "Anomaly Detection in Network Traffic Using Machine Learning Techniques" is as follows This thesis explores the application of machine learning techniques for the detection of anomalies in network traffic. As networks continue to grow and evolve, the need for robust and efficient anomaly detection mechanisms becomes increasingly critical to ensure the security and integrity of network systems. Anomaly detection plays a pivotal role in identifying and mitigating potential threats and intrusions in real-time, thereby safeguarding sensitive data and ensuring the smooth operation of network infrastructures. Chapter 1 provides an introduction to the research topic, outlining the background of the study, defining the problem statement, objectives, limitations, scope, significance, and structure of the thesis. The chapter also presents a comprehensive glossary of key terms to facilitate understanding throughout the document. Chapter 2 consists of a detailed literature review comprising ten critical aspects related to anomaly detection in network traffic using machine learning techniques. This section provides an in-depth analysis of existing research, methodologies, algorithms, and tools employed in anomaly detection within the context of network security. Chapter 3 delves into the research methodology utilized in this study. It encompasses eight key components, including data collection methods, feature selection techniques, model training and evaluation procedures, and performance metrics used to assess the effectiveness of the anomaly detection system. Chapter 4 presents an elaborate discussion of the findings obtained through the implementation of machine learning techniques for anomaly detection in network traffic. The chapter highlights the results, insights, challenges encountered, and potential areas for future research and improvement. Finally, Chapter 5 offers a comprehensive conclusion and summary of the project thesis. This section encapsulates the key findings, contributions, implications, and recommendations derived from the study, emphasizing the significance of employing machine learning techniques for anomaly detection in network traffic. In conclusion, this thesis underscores the critical role of machine learning in enhancing the security and resilience of network systems through effective anomaly detection mechanisms. By leveraging advanced algorithms and methodologies, organizations can proactively identify and mitigate potential threats, safeguarding their networks against malicious activities and ensuring uninterrupted operations.

Thesis Overview