Deep Learning Approaches for Real-Time DDoS Detection in Network-Based Systems: A Comprehensive and Analytical Review

Abstract

Distributed Denial of Service (DDoS) attacks are one of the most important cybersecurity problems today since they are one of the biggest threats against network service continuity and accessibility. Increasing network traffic volume combined with heterogeneous data structures and variations in attack types creates greater complexity in detection and mitigation. Traditional methods based on signatures and static rule sets are not adequate, especially for unknown and evolving attack types. In recent years, deep learning techniques have become a powerful alternative for DDoS detection systems with their ability to automatically extract features from high-volume network traffic data. This study tries to give a detailed comparison of deep learning-based DDoS detection approaches by examining their datasets, model architecture, evaluation metrics, and computational limitations. The review shows that many recent studies have only focused on accuracy; however, critical system parameters such as latency, computational cost, and energy consumption are insufficiently evaluated. Furthermore, generalization problems and dependencies on specific datasets create important vulnerabilities. In this context, this work tries to define future discussions for resource-aware, generalizable, and real-time DDoS detection systems.