mlsanityData to 7 Oct 2026

Adversarial Machine Learning for 5G Communications Security

arXiv 2101.02656 ↗First posted 7 Jan 2021cs.NI cs.LGcited by 12

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Abstract (arXiv)

Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio capabilities empowered by machine learning to perform critical tasks such as spectrum awareness and spectrum sharing, they also become susceptible to new vulnerabilities due to the attacks that target the machine learning applications. This paper identifies the emerging attack surface of adversarial machine learning and corresponding attacks launched against wireless communications in the context of 5G systems. The focus is on attacks against (i) spectrum sharing of 5G communications with incumbent users such as in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer authentication of 5G User Equipment (UE) to support network slicing. For the first attack, the adversary transmits during data transmission or spectrum sensing periods to manipulate the signal-level inputs to the deep learning classifier that is deployed at the Environmental Sensing Capability (ESC) to support the 5G system. For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station. Results indicate major vulnerabilities of 5G systems to adversarial machine learning. To sustain the 5G system operations in the presence of adversaries, a defense mechanism is presented to increase the uncertainty of the adversary in training the surrogate model used for launching its subsequent attacks.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 47 of its 75 references that are in the map. First posted on arXiv 7 Jan 2021.

Builds on

47 of 75 references.

References · 47

  1. 2016Adversarial examples in the physical worldcited by 1,762
  2. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  3. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  4. 2016Adversarial Machine Learning at Scalecited by 934
  5. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  6. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  7. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  8. 2019MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examplescited by 183
  9. 2018Machine Learning with Membership Privacy using Adversarial Regularizationcited by 177
  10. 2019Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inferencecited by 168
  11. 2019Privacy Risks of Securing Machine Learning Models against Adversarial Examplescited by 136
  12. 2017DolphinAtack: Inaudible Voice Commandscited by 65
35 more
  1. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  2. 2020Deep Learning for Wireless Communicationscited by 39
  3. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  4. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  5. 2019Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environmentscited by 28
  6. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  7. 2020Adversarial Machine Learning in Wireless Communications using RF Data: A Reviewcited by 25
  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
  13. 2019Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordinationcited by 17
  14. 2020Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channelscited by 17
  15. 2019Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learningcited by 15
  16. 2020Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofingcited by 14
  17. 2019Spectrum Data Poisoning with Adversarial Deep Learningcited by 13
  18. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13
  19. 2019When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensingcited by 12
  20. 2020When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directionscited by 12
  21. 2018Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensingcited by 11
  22. 2018Generative Adversarial Learning for Spectrum Sensingcited by 10
  23. 2018Active Deep Learning Attacks under Strict Rate Limitations for Online API Callscited by 10
  24. 2019DeepWiFi: Cognitive WiFi with Deep Learningcited by 9
  25. 2020Adversarial Machine Learning based Partial-model Attack in IoTcited by 9
  26. 2020Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifierscited by 9
  27. 2020Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifierscited by 9
  28. 2019Generative Adversarial Networks for Black-Box API Attacks with Limited Training Datacited by 8
  29. 2019Real-Time and Embedded Deep Learning on FPGA for RF Signal Classificationcited by 8
  30. 2019Adversarial Examples in RF Deep Learning: Detection of the Attack and its Physical Robustnesscited by 7
  31. 2019Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-trainingcited by 7
  32. 2020How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacycited by 7
  33. 2019QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learningcited by 6
  34. 2020Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networkscited by 6
  35. 2020Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicingcited by 6

Built on by

Cited by year

Citing papers per year: 2021: 5; 2022: 6; 2023: 1.

12 papers.

  1. 2022Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attackscited by 10
  2. 2021Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicingcited by 6
  3. 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
  4. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  5. 2021Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Casecited by 5
  6. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 4
  7. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  8. 2022Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)cited by 3
  9. 2023Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solutioncited by 2
  10. 2022Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learningcited by 1
  11. 2022Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharingcited by 1
  12. 2022Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Servicescited by 0