Detection and Prevention of DDoS Attacks in Software-Defined Cloud Networks Using Advanced Support Vector Machine
D. Navya Devi, K. Sreenivasulu, M. Janardhan · 2024
Networks for cloud computing have recently focused on Software Defined Clouds (SDC). SDN technology is used in combination with the traditional Cloud network in SDC. SDC attempts to establish an efficient cloud environment throughout the virtualization of all resources. The main problem with SDC Distributed denial of service (DDoS) is a vulnerability. DDoS assault on SDC has become a significant issue, and numerous employed for both mitigation and detection. This essay presents detection and avoidance of DDoS attacks in software-defined cloud advanced support vector machine (ASVM) networks. A three-class multiclass classification approach is the ASVM methodology. In addition, wedescribe a method for utilizing SDN features to identify DDoS attacks in Software Defined Clouds. They analyze the outcomes, by evaluating a Precision, Accuracy, Recall and Detection Rate. The overall accuracy, False alarm rate and Precision values are 97.6%, 97.5% and 97.5% respectively. They demonstrated that the ASVM and transmitted firewalls with IPS security successfully identify and prevent the DDoS attacks based on their simulation results and discussions.