论文标题
对基于DL的大规模MIMO CSI反馈的对抗性攻击
Adversarial Attack on DL-based Massive MIMO CSI Feedback
论文作者
论文摘要
随着深度学习（DL）算法在无线通信中的不断应用，物理层面临着由对抗性攻击引起的新挑战。这种攻击严重影响了计算机视觉中的神经网络。我们选择基于DL的模拟通道状态信息（CSI）来显示对抗攻击对基于DL的通信系统的影响。我们提出了一种对基于DL的CSI反馈过程进行白盒对抗攻击的实用方法。我们的仿真结果表明，通过分析归一化均方根误差的性能，对基于DL的CSI反馈引起的破坏性效应对抗性攻击。我们还发起了一次攻击以进行比较，发现可以通过某些预防措施可以防止干扰攻击。随着DL算法成为开发无线通信的趋势，这项工作引起了人们对使用基于DL的算法的安全性的担忧。
With the increasing application of deep learning (DL) algorithms in wireless communications, the physical layer faces new challenges caused by adversarial attack. Such attack has significantly affected the neural network in computer vision. We chose DL-based analog channel state information (CSI) to show the effect of adversarial attack on DL-based communication system. We present a practical method to craft white-box adversarial attack on DL-based CSI feedback process. Our simulation results showed the destructive effect adversarial attack caused on DL-based CSI feedback by analyzing the performance of normalized mean square error. We also launched a jamming attack for comparison and found that the jamming attack could be prevented with certain precautions. As DL algorithm becomes the trend in developing wireless communication, this work raises concerns regarding the security in the use of DL-based algorithms.
