Anomaly Detection in Network Traffic Using Machine Learning Algorithms

Thesis Abstract

Abstract
In the modern digital era, with the continuous growth of network traffic and the increasing complexity of network systems, the need for effective anomaly detection techniques has become paramount. Anomaly detection plays a crucial role in ensuring the security and stability of network infrastructures by identifying unusual patterns or behaviors that may indicate malicious activities or system failures. This thesis focuses on the application of machine learning algorithms for anomaly detection in network traffic, aiming to enhance the accuracy and efficiency of detecting abnormal network behavior. The research begins with a comprehensive literature review to explore existing methodologies and approaches to anomaly detection in network traffic. Various machine learning algorithms such as Support Vector Machines, Random Forest, and Neural Networks are examined in terms of their applicability and effectiveness in detecting anomalies in network data. Subsequently, the research methodology chapter details the process of data collection, preprocessing, feature selection, and model training for anomaly detection. The study utilizes a real-world network traffic dataset to evaluate the performance of different machine learning algorithms in detecting anomalies accurately and efficiently. The findings chapter presents a detailed analysis of the experimental results, comparing the performance of various machine learning algorithms in terms of detection accuracy, false positive rate, and computational efficiency. The discussion delves into the strengths and limitations of each algorithm, highlighting their potential for practical implementation in real-world network environments. In conclusion, this research contributes to the field of anomaly detection in network traffic by demonstrating the effectiveness of machine learning algorithms in identifying abnormal patterns and behaviors. The study provides valuable insights into the application of advanced computational techniques for enhancing network security and reliability. The findings of this thesis have implications for network administrators, cybersecurity professionals, and researchers working in the field of network security. Overall, this thesis underscores the importance of leveraging machine learning algorithms for anomaly detection in network traffic to mitigate security risks, enhance system performance, and ensure the integrity of network infrastructures in the face of evolving cyber threats.