mlsanityData to 7 Oct 2026

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

arXiv 2103.13989 ↗First posted 25 Mar 2021eess.SP cs.LG cs.NIcited by 6

Not on the map. Why?

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

28 of 43 references.

References · 28

  1. 2013Intriguing properties of neural networkscited by 5,114
  2. 2016Adversarial examples in the physical worldcited by 1,762
  3. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  4. 2020Deep Learning for Wireless Communicationscited by 39
  5. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  6. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  7. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  8. 2019Generative Adversarial Network for Wireless Signal Spoofingcited by 21
  9. 2019IoT Network Security from the Perspective of Adversarial Deep Learningcited by 20
  10. 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
  11. 2019Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systemscited by 19
  12. 2019The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detectioncited by 17
16 more
  1. 2020Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channelscited by 17
  2. 2019Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learningcited by 15
  3. 2020Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofingcited by 14
  4. 2019Spectrum Data Poisoning with Adversarial Deep Learningcited by 13
  5. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13
  6. 2019When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensingcited by 12
  7. 2020When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directionscited by 12
  8. 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
  9. 2021Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Networkcited by 11
  10. 2020Adversarial Machine Learning based Partial-model Attack in IoTcited by 9
  11. 2020Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifierscited by 9
  12. 2020Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifierscited by 9
  13. 2016Initial Access in 5G mm-Wave Cellular Networkscited by 8
  14. 2020How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacycited by 7
  15. 2020Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networkscited by 6
  16. 2021Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicingcited by 6

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 1.

6 papers.

  1. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 8
  2. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  3. 2022Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examplescited by 5
  4. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 4
  5. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  6. 2023Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systemscited by 1