Dynamic graph neural network-based framework to increase detection accuracy in SDN under DDOS.

ABSTRACT: Software-Defined Networking (SDN) has changed the way networks are managed by providing a centralized and programmable framework. However, it also makes the SDN controller more vulnerable to Distributed Denial of Service (DDoS) attacks, which try to take it down. Current intrusion detection technologies, encompassing conventional machine learning and deep learning models, frequently encounter difficulties in adapting to evolving attack patterns or effectively capturing the intricate topological features inherent in SDN environments. To overcome these limitations, GCTNetwork is proposed as an innovative framework grounded in Dynamic Graph Neural Networks (DGNN) designed to identify DDoS attacks in Software-Defined Networks (SDNs) in real time. GCTNetwork utilizes Gated Convolutional Tempora

SUBMITTER: Kalafy SAA 

PROVIDER: S-EPMC12816605 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature