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

, , and . BlackSeaCom, page 1-6. IEEE, (2021)

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The effect of secondary user locations on the cooperative detection performance., and . SIU, page 1-4. IEEE, (2012)Multi-user shared access in massive machine-type communication systems via superimposed waveforms., , , and . Phys. Commun., (2019)Enhanced physical layer security by OFDM signal transmission in fractional Fourier domains., , and . SIU, page 1881-1884. IEEE, (2015)Security concerns on machine learning solutions for 6G networks in mmWave beam prediction., , , , and . Phys. Commun., (2022)Adaptive filterbank-based multi-carrier waveform design for flexible data rates., and . Comput. Electr. Eng., (2017)Adversarial security mitigations of mmWave beamforming prediction models using defensive distillation and adversarial retraining., , , , and . Int. J. Inf. Sec., 22 (2): 319-332 (April 2023)Filtered multitone system for users with different data rates at 5G wireless networks., and . SIU, page 741-744. IEEE, (2016)The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining., , , , and . CoRR, (2022)Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals., , , , and . GLOBECOM (Workshops), page 1371-1376. IEEE, (2022)Defending AI-Based Automatic Modulation Recognition Models Against Adversarial Attacks., , , , and . IEEE Access, (2023)