Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training

by   B. R. Manoj, et al.

The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has been much work demonstrating the susceptibility of DL-based classification tasks to adversarial attacks, regression-based problems in the context of a wireless system have not been studied so far from an attack perspective. The aim of this paper is twofold: (i) we consider a regression problem in a wireless setting and show that adversarial attacks can break the DL-based approach and (ii) we analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly. Specifically, the wireless application considered in this paper is the DL-based power allocation in the downlink of a multicell massive multi-input-multi-output system, where the goal of the attack is to yield an infeasible solution by the DL model. We extend the gradient-based adversarial attacks: fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent method to analyze the susceptibility of the considered wireless application with and without adversarial training. We analyze the deep neural network (DNN) models performance against these attacks, where the adversarial perturbations are crafted using both the white-box and black-box attacks.


page 1

page 9


Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network

Deep learning (DL) is becoming popular as a new tool for many applicatio...

Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System

Deep learning (DL) architectures have been successfully used in many app...

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

The design of a security scheme for beamforming prediction is critical f...

Adversarial Robustness of Deep Convolutional Candlestick Learner

Deep learning (DL) has been applied extensively in a wide range of field...

Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network

In distributed multiple-input multiple-output (D-MIMO) networks, power c...

CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning

This paper proposes CuRTAIL, an end-to-end computing framework for chara...

Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks

Effective detection of energy theft can prevent revenue losses of utilit...

Please sign up or login with your details

Forgot password? Click here to reset