Thesis Abstract

Abstract
The rapid growth of network technologies has led to an increase in cyber threats, making it critical for organizations to detect and mitigate anomalies in network traffic. Anomaly detection plays a crucial role in identifying suspicious activities that deviate from normal patterns, allowing for timely response and prevention of security breaches. This thesis focuses on the application of machine learning algorithms for anomaly detection in network traffic to enhance cybersecurity measures. The research begins with an introduction to the significance of anomaly detection in network security and the challenges posed by evolving cyber threats. A comprehensive literature review is conducted to explore existing techniques and methodologies for anomaly detection in network traffic. This review highlights the strengths and limitations of various machine learning algorithms commonly used in anomaly detection, such as Support Vector Machines, Random Forest, and Neural Networks. The research methodology section outlines the process of collecting network traffic data, preprocessing the data for analysis, and training machine learning models for anomaly detection. Various evaluation metrics are employed to assess the performance of the models, including accuracy, precision, recall, and F1 score. The methodology also includes a discussion on feature selection techniques and model tuning to optimize the detection of network anomalies. The findings of the study reveal the effectiveness of machine learning algorithms in accurately detecting anomalies in network traffic. Through experimental evaluations, it is demonstrated that certain algorithms outperform others in terms of detection accuracy and computational efficiency. The discussion of findings delves into the factors influencing the performance of machine learning models, such as dataset size, feature selection, and model complexity. In conclusion, this thesis emphasizes the importance of leveraging machine learning algorithms for anomaly detection in network traffic to enhance cybersecurity measures. By implementing robust detection mechanisms, organizations can proactively identify and respond to potential security threats, safeguarding their networks and sensitive data. The research contributes to the advancement of anomaly detection techniques in the field of cybersecurity and provides valuable insights for future research and practical implementations. Keywords Anomaly Detection, Network Traffic, Machine Learning Algorithms, Cybersecurity, Evaluation Metrics, Feature Selection, Model Optimization.

Thesis Overview