Study of Deep Learning-based methods for DDoS attack detection

Abstract

This state-of-the-art provides a comprehensive examination of strategies to enhance the security of Software Defined Networking (SDN) environments, focusing on advanced anomaly detection methods to combat Distributed Denial of Service (DDoS) attacks. Emphasizing the utilization of different Deep Learning (DL) techniques, this paper presents a thorough exploration of frameworks aimed at identifying and mitigating DDoS threats within SDN infrastructures. We propose three different faceted taxonomies to delve into DDoS attack types, explore DL-based approaches for DDoS detection, and examine the available tools (software tools and datasets) that can be exploited for improving the detection of this type of attacks.