TY - EJOU AU - Fadel, Magdy M. AU - El-Ghamrawy, Sally M. AU - Ali-Eldin, Amr M. T. AU - Hassan, Mohammed K. AU - El-Desoky, Ali I. TI - HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework T2 - Computers, Materials \& Continua PY - 2022 VL - 73 IS - 2 SN - 1546-2226 AB - Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the entire network will fail. This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention (HDLIDP) framework, which blends signature-based and deep learning neural networks to detect and prevent intrusions. This framework improves detection accuracy while addressing all of the aforementioned problems. To validate the framework, experiments are done on both traditional and SDN datasets; the findings demonstrate a significant improvement in classification accuracy. KW - Software defined networks (SDN); distributed denial of service attack (DDoS); signature-based detection; whale optimization algorism (WOA); deep learning neural network classifier DO - 10.32604/cmc.2022.028287