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This paper advances the state of the art by proposing the first comprehensive analysis and experimental evaluation of adversarial learning attacks to wireless deep learning systems.
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F. Restuccia and T. Melodia, “Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking through Deep Learning in the RF Loop,” in
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F. Restuccia, S. D’Oro, A. Al-Shawabka, M. Belgiovine, L. Angioloni, S. Ioannidis, K. Chowdhury, and T. Melodia, “DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms,”
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J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, and T. Melodia, “Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey,”
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2017
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T. J. O’Shea, T. Roy, and T. C. Clancy, “Over-the-Air Deep Learning Based Radio Signal Classification,”
2018
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F. Restuccia, S. D’Oro, and T. Melodia, “Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking,”
2018
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Y. Dong, F. Liao, T. Pang, H. Su, J. Zhu, X. Hu, and J. Li, “Boosting Adversarial Attacks with Momentum,” in
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S. Riyaz, K. Sankhe, S. Ioannidis, and K. Chowdhury, “Deep Learning Convolutional Neural Networks for Radio Identification,”
2018
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2019
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Y. Shi, K. Davaslioglu, and Y. E. Sagduyu, “Generative Adversarial Network for Wireless Signal Spoofing,” in
2019
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S. Bair, M. DelVecchio, B. Flowers, A. J. Michaels, and W. C. Headley, “On the Limitations of Targeted Adversarial Evasion Attacks Against Deep Learning Enabled Modulation Recognition,” in
2019
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M. Sadeghi and E. G. Larsson, “Adversarial Attacks on Deep-Learning Based Radio Signal Classification,”
2019
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F. Restuccia and T. Melodia, “PolymoRF: Polymorphic Wireless Receivers Through Physical-Layer Deep Learning,”
2020
Closest in time.
F. Restuccia, S. D’Oro, A. Al-Shawabka, B. Costa Rendon, K. Chowdhury, S. Ioannidis, and T. Melodia, “Generalized Wireless Adversarial Deep Learning,”
2020
Closest in time.