# Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case

arXiv: https://arxiv.org/abs/2103.07268 · HTML: https://mlsanity.com/paper/2103.07268/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples](https://mlsanity.com/paper/2102.04150/) (2021)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Beam-space Multiplexing: Practice, Theory, and Trends-From 4G TD-LTE, 5G, to 6G and Beyond](https://mlsanity.com/paper/2001.05021/) (2020)

## Built on by (most cited first, 5 of 5)

- [Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction](https://mlsanity.com/paper/2105.03905/) (2021)
- [The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining](https://mlsanity.com/paper/2202.08185/) (2022)
- [Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks](https://mlsanity.com/paper/2208.10279/) (2022)
- [Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals](https://mlsanity.com/paper/2209.13007/) (2022)
- [Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services](https://mlsanity.com/paper/2505.01460/) (2025)

## Abstract (arXiv)

6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account to apply the algorithms. While machine learning offers significant advantages for 6G, AI models' security is ignored. Since it has many applications in the real world, security is a vital part of the algorithms. This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction use case. We have also presented the adversarial learning mitigation method's performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model and undefended model are very close.