Convolutional Autoencoders Coupled With Hypernetworks for Recognizing Attacks in 5G Networks and Beyond

The evolution of Beyond 5G (B5G) and 6G networks requires the design of powerful deep neural networks to effectively address the emerging threats and ensure predictive, adaptive security measures that can safeguard the performance and integrity of these advanced systems. Existing studies rely on fully supervised learning settings, lack mechanisms for dynamic adaptation, and perform their experiments on datasets, which do not represent the characteristics of existing 5G scenarios. To address these limitations, we present the first study incorporating hypernetworks into convolutional autoencoders for identifying attacks in 5G datasets collected under realistic conditions. Specifically, the input data is reshaped into a 2D matrix and fed into a convolutional autoencoder, consisting of an encoder and a decoder. At the same time, raw input data is passed through a hypernetwork, which is trained to generate weights for the target network. The target network receives as input the latent representation vector and gives as output the final prediction. Experiments are performed on two real 5G network datasets, namely NANCY and 5G-NIDD. Results show that the proposed deep neural network achieves an Accuracy of 98.51% and 99.91% on the NANCY and 5G-NIDD datasets respectively. Findings of an ablation study demonstrate the effectiveness of the proposed method.