# Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond

arXiv: https://arxiv.org/abs/2103.13989 · HTML: https://mlsanity.com/paper/2103.13989/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 28 of 28)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications](https://mlsanity.com/paper/1903.01563/) (2019)
- [Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers](https://mlsanity.com/paper/2005.05321/) (2020)
- [Generative Adversarial Network for Wireless Signal Spoofing](https://mlsanity.com/paper/1905.01008/) (2019)
- [IoT Network Security from the Perspective of Adversarial Deep Learning](https://mlsanity.com/paper/1906.00076/) (2019)
- [Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks](https://mlsanity.com/paper/1911.00500/) (2019)
- [Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems](https://mlsanity.com/paper/1902.08391/) (2019)
- [The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection](https://mlsanity.com/paper/1902.10674/) (2019)
- [Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels](https://mlsanity.com/paper/2002.02400/) (2020)
- [Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning](https://mlsanity.com/paper/1910.10766/) (2019)
- [Spectrum Data Poisoning with Adversarial Deep Learning](https://mlsanity.com/paper/1901.09247/) (2019)
- [Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing](https://mlsanity.com/paper/2007.08363/) (2020)
- [Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers](https://mlsanity.com/paper/2006.14576/) (2020)
- [When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing](https://mlsanity.com/paper/1905.01430/) (2019)
- [When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions](https://mlsanity.com/paper/2001.08883/) (2020)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network](https://mlsanity.com/paper/2101.12090/) (2021)
- [Adversarial Machine Learning based Partial-model Attack in IoT](https://mlsanity.com/paper/2006.14146/) (2020)
- [Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers](https://mlsanity.com/paper/2007.16204/) (2020)
- [Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers](https://mlsanity.com/paper/2012.02160/) (2020)
- [Initial Access in 5G mm-Wave Cellular Networks](https://mlsanity.com/paper/1602.07731/) (2016)
- [How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy](https://mlsanity.com/paper/2005.07675/) (2020)
- [Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks](https://mlsanity.com/paper/2006.12653/) (2020)
- [Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing](https://mlsanity.com/paper/2101.08724/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Jamming Attacks on Federated Learning in Wireless Networks](https://mlsanity.com/paper/2201.05172/) (2022)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)

## Abstract (arXiv)

Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications. To establish the IA between the base station (e.g., gNodeB) and user equipment (UE) for directional transmissions, a deep neural network (DNN) can predict the beam that is best slanted to each UE by using the received signal strengths (RSSs) from a subset of possible narrow beams. While improving the latency and reliability of beam selection compared to the conventional IA that sweeps all beams, the DNN itself is susceptible to adversarial attacks. We present an adversarial attack by generating adversarial perturbations to manipulate the over-the-air captured RSSs as the input to the DNN. This attack reduces the IA performance significantly and fools the DNN into choosing the beams with small RSSs compared to jamming attacks with Gaussian or uniform noise.