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

arXiv: https://arxiv.org/abs/2110.04731 · HTML: https://mlsanity.com/paper/2110.04731/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 12 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models](https://mlsanity.com/paper/2002.02175/) (2020)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Deep Learning Power Allocation in Massive MIMO](https://mlsanity.com/paper/1812.03640/) (2018)
- [Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems](https://mlsanity.com/paper/2102.00918/) (2021)
- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [Practical Attacks Against Privacy and Availability in 4G/LTE Mobile Communication Systems](https://mlsanity.com/paper/1510.07563/) (2015)

## Built on by (most cited first, 3 of 3)

- [Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey](https://mlsanity.com/paper/2303.06302/) (2023)
- [A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)](https://mlsanity.com/paper/2401.13751/) (2024)
- [Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network](https://mlsanity.com/paper/2301.09305/) (2023)

## Abstract (arXiv)

Deep learning (DL) architectures have been successfully used in many applications including wireless systems. However, they have been shown to be susceptible to adversarial attacks. We analyze DL-based models for a regression problem in the context of downlink power allocation in massive multiple-input-multiple-output systems and propose universal adversarial perturbation (UAP)-crafting methods as white-box and black-box attacks. We benchmark the UAP performance of white-box and black-box attacks for the considered application and show that the adversarial success rate can achieve up to 60% and 40%, respectively. The proposed UAP-based attacks make a more practical and realistic approach as compared to classical white-box attacks.