Enhancing DDoS Attack Classification: Ensemble of ANN Models with SDN-Based Network Analysis

In this survey optimization of Distributed Denial of Service (DDoS) attack classification has been explored. Dealing with the increasing frequency, and complexity of such attacks presents a substantial challenge to contemporary network infrastructures. An ensemble approach leveraging Artificial Neural Network (ANN) models is proposed to enhance accuracy and robustness. The integration of multiple neural network architectures, combined with the introduction of Software-Defined Networking (SDN), is emphasized as a crucial element for efficient network data analysis. SDN provides dynamic and programmable network management with real-time response capabilities. The fusion of ANN models and SDN-based analytics aims to improve DDoS attack detection precision while minimizing false positives. Thoroughly scrutinizing the strengths and weaknesses of diverse ANN architectures to assess their suitability for DDoS attack classification, the study has highlighted the impact of SDN on adaptability and responsiveness in network security. Positioning the ensemble approach as a comprehensive defense mechanism against the evolving DDoS threat landscape, the research illuminates not only the then-current state of DDoS attack classification, but also a proposed forward-looking methodology laying the groundwork for intelligent and adaptive security solutions aimed at safeguarding network infrastructures against persistent DDoS onslaughts