Intelligent DDoS Attack Detection in 6G Cloud Networks Using a CNN-GRU Hybrid Model

The IoV facilitates both intra- and inter-vehicle communication for expanding IoT applications and is integral to the globally interconnected 6G HetNet, which includes terrestrial, aerial, and underwater networks. However, significant security issues arise from transitioning to an extensive cellular infrastructure, particularly concerning 6G cloud networks and distributed denial of service attacks. This issue can only be addressed with a more sophisticated mechanism for identifying attacks. This paper presents a novel approach utilising a hybrid DL model that combines a GRU with a CNN. A BFS technique is employed to discern essential attributes following data preprocessing through filtering, transformation, and normalisation. A Softmax activation layer is employed to integrate and classify the input data subsequent to the collaborative processing of the CNN and GRU, which capture diverse perspectives of the input data. The CNN-GRU model surpasses state-of-the-art techniques in DDoSdetection attack, as experimental evaluations indicate an accuracy of 99.24%. The integration of satellite networks inside the 6G framework guarantees dependable large-scale data dissemination, especially in disaster recovery scenarios. To address evolving cyber threats, future research should explore real-time adaptability and enhanced defensive strategies; our study underscores the significance of robust security protocols in 6G cloud networks.