Authors: Mrs . Kolli Kundana Bhavya Sree, Mrs. B Sirisha, Mtech, Associate Professor
Abstract: To ensure the reliability of connected devices, machine learning is employed to analyse network traffic, facilitating quicker identification of unusual behaviour and congestion. The application of machine learning methods improves the ability to manage traffic and supports the maintenance of service quality. Furthermore, the role of machine learning in network security is to identify anomalies and classify traffic in real-time, aiming to optimize network performance and uncover potential threats. This study highlights the beneficial effects of utilizing machine learning techniques to improve network reliability and security. One of our contributions is an examination of an example of HTTP/3 traffic interacting with a web server. We implemented machine learning algorithms to differentiate between standard traffic and possible HTTP/3 flood attacks. Additionally, we developed a dataset of traffic samples featuring 23 attributes categorized into six subgroups. From traffic captured in a simulated environment, we evaluated the significance of these attributes and discovered that employing machine learning techniques can greatly enhance both network security and reliability. We utilized four supervised classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbours (KNN). These algorithms represent a category of supervised classification methods. They played a crucial role in training datasets of network traffic, which were carefully labelled to distinguish between Distributed Denial-of-Service (DDoS) attacks and normal traffic. The results of this research demonstrate the efficacy of machine learning algorithms in analysing network traffic to detect specific types of DDoS attacks, especially those that use QUIC traffic. This illustrates the significant potential of machine learning techniques in strengthening the overall security and reliability of networks.
DOI: http://doi.org/
