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Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces

arXiv 2112.11414 ↗First posted 21 Dec 2021eess.SP cs.LG cs.NIcited by 0

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Abstract (arXiv)

By moving from massive antennas to antenna surfaces for software-defined wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals, mitigating the propagation loss and multipath attenuation, and thereby improving the coverage and spectral efficiency. In this paper, covert communication is considered in the presence of the RIS. While there is an ongoing transmission boosted by the RIS, both the intended receiver and an eavesdropper individually try to detect this transmission using their own deep neural network (DNN) classifiers. The RIS interaction vector is designed by balancing two (potentially conflicting) objectives of focusing the transmitted signal to the receiver and keeping the transmitted signal away from the eavesdropper. To boost covert communications, adversarial perturbations are added to signals at the transmitter to fool the eavesdropper's classifier while keeping the effect on the receiver low. Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 21 Dec 2021.

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27 of 33 references.

References · 27

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  4. 2020Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learningcited by 39
  5. 2020Deep Learning for Wireless Communicationscited by 39
  6. 2019Wireless Communications Through Reconfigurable Intelligent Surfacescited by 37
  7. 2018Deep Learning for Launching and Mitigating Wireless Jamming Attackscited by 31
  8. 2019Evaluating Adversarial Evasion Attacks in the Context of Wireless Communicationscited by 28
  9. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  10. 2020Adversarial Machine Learning in Wireless Communications using RF Data: A Reviewcited by 25
  11. 2019Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learningcited by 20
  12. 2019Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attackscited by 20
15 more
  1. 2019The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detectioncited by 17
  2. 2020Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channelscited by 17
  3. 2019Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learningcited by 15
  4. 2020Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifierscited by 13
  5. 2019Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfacescited by 12
  6. 2020When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directionscited by 12
  7. 2020Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operationcited by 11
  8. 2018A New Wireless Communication Paradigm through Software-controlled Metasurfacescited by 9
  9. 2020Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifierscited by 9
  10. 2020Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifierscited by 9
  11. 2020How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacycited by 7
  12. 2021Deep Learning for THz Drones with Flying Intelligent Surfaces: Beam and Handoff Predictioncited by 4
  13. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  14. 2021Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfacescited by 3
  15. 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3

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