GAN-based Generator of Adversarial Attack on Intelligent End-to-End Autoencoder-based Communication System

TL;DR

This paper introduces a GAN-based adversarial attack model targeting autoencoder-based wireless communication systems, which does not require prior knowledge of the target and effectively increases block error rates across various channel conditions.

Contribution

The paper presents a novel GAN architecture for attack generation that operates without target information and demonstrates superior attack performance in wireless channels.

Findings

Achieves higher block error rates than benchmark schemes.

Effective across AWGN, Rayleigh, and high-speed railway channels.

Utilizes a new training and validation strategy for the attack generator.

Abstract

Deep neural networks have been applied in wireless communications system to intelligently adapt to dynamically changing channel conditions, while the users are still under the threat of the malicious attacks due to the broadcasting property of wireless channels. However, most attack models require the knowledge of the target details, which is difficult to be implemented in real systems. Our objective is to develop an attack model with no requirement for the target information, while enhancing the block error rate. In our design, we propose a novel Generative Adversarial Networks(GANs) based attack architecture, which exploits the property of deep learning models being vulnerable to perturbations induced by dynamically changing channel conditions. In the proposed generator, the attack network is composed of convolution layer, convolution transpose layer and linear layer. Then we present the training strategy and the details of the training algorithm. Subsequently, we propose the validation strategy to evaluate the performance of the generator. Simulations are conducted and the results show that our proposed adversarial attack generator achieve better block error rate attack performance than that of benchmark schemes over Additive White Gaussian Noise (AWGN) channel, Rayleigh channel and High-Speed Railway channel.