Analysis of Deep and Graph Neural Networks for Enhanced DDoS Attack Detection: A Pathway to Hybrid Models

Abstract

Distributed denial-of-service (DDoS) attacks pose significant threats to network security, targeting critical infrastructures with increasing complexity. Traditional defense mechanisms that depend on static rules and signatures struggle to address the dynamic nature of these attacks. Software-defined networking (SDN), integrated with machine learning (ML) and deep learning (DL) techniques, has emerged as a promising solution for DDoS detection and mitigation. This paper presents a comparative analysis of deep neural networks (DNNs) and graph neural networks (GNNs) within an SDN framework to detect DDoS attacks. Using real-time network monitoring and traffic anomaly identification, the models are evaluated using metrics such as accuracy, precision, recall, and F1-score against both known and novel attack patterns. Our findings underscore the superiority of GNNs in capturing complex and interconnected relationships within network traffic, offering enhanced detection capabilities. This study contributes to adaptive cybersecurity strategies, advancing the application of ML models in combating evolving cyber threats.