GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems

Abstract

Generative adversarial networks (GANs), a category of deep learning models, have become a cybersecurity concern for wireless communication systems. These networks enable potential attackers to deceive receivers that rely on convolutional neural networks (CNNs) by transmitting deceptive wireless signals that are statistically indistinguishable from genuine ones. While GANs have been used before for digitally modulated single-carrier waveforms, this study explores their applicability to model filtered multi-carrier waveforms, such as orthogonal frequency-division multiplexing (OFDM), filtered orthogonal FDM (F-OFDM), generalized FDM (GFDM), filter bank multi-carrier (FBMC), and universal filtered MC (UFMC). In this research, an evasion attack is conducted using GAN-generated counterfeit filtered multi-carrier signals to trick the target receiver. The results show that there is a remarkable 99.7% probability of the receiver misclassifying these GAN-based fabricated signals as authentic ones. This highlights the need for urgent investigation into the development of preventive measures to address this concerning vulnerability.