Detection of DDOS Attacks in Cloud Environment Using Deep Learning

The rapid evolution of cloud computing and the increasing reliance on Software -Defined Networking (SDN) have introduced new challenges in ensuring the security and availability of cloud based services. Distributed Denial of Service (DDoS) attacks, in particular, pose a significant threat to the stability and performance of cloud infrastructures. As network traffic patterns continue to increase and evolve, it is crucial to extract pertinent features that aid in identifying attacks and improving the accuracy of detection. Therefore, effective detection mechanisms are crucial to identify and mitigate DDoS attacks in SDN-based cloud environments. This study presents a novel approach that combines Decision Tree with Long Short-Term Memory (LSTM) for effective DDoS detection in cloud infrastructures. The model is evaluated using the 2019 dataset. The simulation findings validateits efficacy and show that it improves upon existing works in terms of accuracy.